{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SafeML Implementation for German Traffic Sign Recognition\n",
    "In this example, we try to show how SafeML approach can be used for German Traffic Sign Recognition.We have used an existing code from the following URL that do the traffic sign classification using Convolutional Neural Networks (CNNs) with about 97% accuracy. Then the SafeML part is added to show how the safety of the approach can be monitored:\n",
    "\n",
    "Source: https://www.kaggle.com/lalithmovva/99-accuracy-on-german-traffic-sign-recognition\n",
    "\n",
    "Here is the table of content:\n",
    "* [Defining the required libraries and loading the german traffic sgin recognition dataset.](#section-one)\n",
    "* Separating Train, Test and Validation Data\n",
    "* Defining the CNN model and its architecture.\n",
    "* Training the model and calculating its accuracy\n",
    "* Applying the model on test data\n",
    "* Comparing the true labels with predicted labels and using the statistical parametric mapping (as the SafeML method)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id="section-one"></a>
    ## Defining the required libraries and loading the german traffic sgin recognition dataset.\n",
    "Before running the code, the dataset should be downloaded an stored on the local computer. Please make sure that the path has been chosen correctly. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:/cmder/Python_Tests/GTSRB/Train/0/\n",
      "C:/cmder/Python_Tests/GTSRB/Train/1/\n",
      "C:/cmder/Python_Tests/GTSRB/Train/2/\n",
      "C:/cmder/Python_Tests/GTSRB/Train/3/\n",
      "C:/cmder/Python_Tests/GTSRB/Train/4/\n",
      "C:/cmder/Python_Tests/GTSRB/Train/5/\n",
      "C:/cmder/Python_Tests/GTSRB/Train/6/\n",
      "C:/cmder/Python_Tests/GTSRB/Train/7/\n",
      "C:/cmder/Python_Tests/GTSRB/Train/8/\n",
      "C:/cmder/Python_Tests/GTSRB/Train/9/\n",
      "C:/cmder/Python_Tests/GTSRB/Train/10/\n",
      "C:/cmder/Python_Tests/GTSRB/Train/11/\n",
      "C:/cmder/Python_Tests/GTSRB/Train/12/\n",
      "C:/cmder/Python_Tests/GTSRB/Train/13/\n",
      "C:/cmder/Python_Tests/GTSRB/Train/14/\n",
      "C:/cmder/Python_Tests/GTSRB/Train/15/\n",
      "C:/cmder/Python_Tests/GTSRB/Train/16/\n",
      "C:/cmder/Python_Tests/GTSRB/Train/17/\n",
      "C:/cmder/Python_Tests/GTSRB/Train/18/\n",
      "C:/cmder/Python_Tests/GTSRB/Train/19/\n",
      "C:/cmder/Python_Tests/GTSRB/Train/20/\n",
      "C:/cmder/Python_Tests/GTSRB/Train/21/\n",
      "C:/cmder/Python_Tests/GTSRB/Train/22/\n",
      "C:/cmder/Python_Tests/GTSRB/Train/23/\n",
      "C:/cmder/Python_Tests/GTSRB/Train/24/\n",
      "C:/cmder/Python_Tests/GTSRB/Train/25/\n",
      "C:/cmder/Python_Tests/GTSRB/Train/26/\n",
      "C:/cmder/Python_Tests/GTSRB/Train/27/\n",
      "C:/cmder/Python_Tests/GTSRB/Train/28/\n",
      "C:/cmder/Python_Tests/GTSRB/Train/29/\n",
      "C:/cmder/Python_Tests/GTSRB/Train/30/\n",
      "C:/cmder/Python_Tests/GTSRB/Train/31/\n",
      "C:/cmder/Python_Tests/GTSRB/Train/32/\n",
      "C:/cmder/Python_Tests/GTSRB/Train/33/\n",
      "C:/cmder/Python_Tests/GTSRB/Train/34/\n",
      "C:/cmder/Python_Tests/GTSRB/Train/35/\n",
      "C:/cmder/Python_Tests/GTSRB/Train/36/\n",
      "C:/cmder/Python_Tests/GTSRB/Train/37/\n",
      "C:/cmder/Python_Tests/GTSRB/Train/38/\n",
      "C:/cmder/Python_Tests/GTSRB/Train/39/\n",
      "C:/cmder/Python_Tests/GTSRB/Train/40/\n",
      "C:/cmder/Python_Tests/GTSRB/Train/41/\n",
      "C:/cmder/Python_Tests/GTSRB/Train/42/\n"
     ]
    }
   ],
   "source": [
    "#Source: https://www.kaggle.com/lalithmovva/99-accuracy-on-german-traffic-sign-recognition\n",
    "# Libraries \n",
    "import numpy as np \n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "import cv2\n",
    "from PIL import Image\n",
    "import os\n",
    "\n",
    "# Reading the input images and putting them into a numpy array\n",
    "data=[]\n",
    "labels=[]\n",
    "\n",
    "height = 30\n",
    "width = 30\n",
    "channels = 3\n",
    "classes = 43\n",
    "n_inputs = height * width*channels\n",
    "\n",
    "for i in range(classes) :\n",
    "    path = \"C:/cmder/Python_Tests/GTSRB/Train/{0}/\".format(i)\n",
    "    print(path)\n",
    "    Class=os.listdir(path)\n",
    "    for a in Class:\n",
    "        try:\n",
    "            image=cv2.imread(path+a)\n",
    "            image_from_array = Image.fromarray(image, 'RGB')\n",
    "            size_image = image_from_array.resize((height, width))\n",
    "            data.append(np.array(size_image))\n",
    "            labels.append(i)\n",
    "        except AttributeError:\n",
    "            print(\" \")\n",
    "            \n",
    "Cells=np.array(data)\n",
    "labels=np.array(labels)\n",
    "\n",
    "#Randomize the order of the input images\n",
    "s=np.arange(Cells.shape[0])\n",
    "np.random.seed(43)\n",
    "np.random.shuffle(s)\n",
    "Cells=Cells[s]\n",
    "labels=labels[s]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Separating Train, Test and Validation Data\n",
    "In this section, 20% of data is separated for test and validation and the rest is used for training. This section can be improved using methods like K-fold cross-validation. We tried to make it as simple as possible."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "#Spliting the images into train and validation sets\n",
    "(X_train,X_val)=Cells[(int)(0.2*len(labels)):],Cells[:(int)(0.2*len(labels))]\n",
    "X_train = X_train.astype('float32')/255 \n",
    "X_val = X_val.astype('float32')/255\n",
    "(y_train,y_val)=labels[(int)(0.2*len(labels)):],labels[:(int)(0.2*len(labels))]\n",
    "\n",
    "#Using one hote encoding for the train and validation labels\n",
    "from keras.utils import to_categorical\n",
    "\n",
    "y_train = to_categorical(y_train, 43)\n",
    "y_val = to_categorical(y_val, 43)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Defining the CNN model and its architecture\n",
    "Here the CNN model's structure is defined. Regarding the loss function the categorical crossentropy is used, and the ADAM is selection for the network optimization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\ProgramData\\Miniconda3\\lib\\site-packages\\tensorflow_core\\python\\ops\\resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "If using Keras pass *_constraint arguments to layers.\n",
      "WARNING:tensorflow:From C:\\ProgramData\\Miniconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:4070: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Definition of the DNN model\n",
    "\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Conv2D, MaxPool2D, Dense, Flatten, Dropout\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Conv2D(filters=32, kernel_size=(5,5), activation='relu', input_shape=X_train.shape[1:]))\n",
    "model.add(Conv2D(filters=64, kernel_size=(3, 3), activation='relu'))\n",
    "model.add(MaxPool2D(pool_size=(2, 2)))\n",
    "model.add(Dropout(rate=0.25))\n",
    "model.add(Conv2D(filters=64, kernel_size=(3, 3), activation='relu'))\n",
    "model.add(MaxPool2D(pool_size=(2, 2)))\n",
    "model.add(Dropout(rate=0.25))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(256, activation='relu'))\n",
    "model.add(Dropout(rate=0.5))\n",
    "model.add(Dense(43, activation='softmax'))\n",
    "\n",
    "#Compilation of the model\n",
    "model.compile(\n",
    "    loss='categorical_crossentropy', \n",
    "    optimizer='adam', \n",
    "    metrics=['accuracy']\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training the Model and calculating its accuracy\n",
    "In this section, the CNN model is trained and the accuracy plot is shown. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\ProgramData\\Miniconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:422: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
      "\n",
      "Train on 31368 samples, validate on 7841 samples\n",
      "Epoch 1/20\n",
      "31368/31368 [==============================] - 71s 2ms/step - loss: 1.2010 - accuracy: 0.6612 - val_loss: 0.1371 - val_accuracy: 0.9707\n",
      "Epoch 2/20\n",
      "31368/31368 [==============================] - 70s 2ms/step - loss: 0.2166 - accuracy: 0.9350 - val_loss: 0.0570 - val_accuracy: 0.9853\n",
      "Epoch 3/20\n",
      "31368/31368 [==============================] - 70s 2ms/step - loss: 0.1288 - accuracy: 0.9621 - val_loss: 0.0384 - val_accuracy: 0.9907\n",
      "Epoch 4/20\n",
      "31368/31368 [==============================] - 68s 2ms/step - loss: 0.1004 - accuracy: 0.9699 - val_loss: 0.0490 - val_accuracy: 0.9878\n",
      "Epoch 5/20\n",
      "31368/31368 [==============================] - 73s 2ms/step - loss: 0.0808 - accuracy: 0.9753 - val_loss: 0.0425 - val_accuracy: 0.9893\n",
      "Epoch 6/20\n",
      "31368/31368 [==============================] - 74s 2ms/step - loss: 0.0745 - accuracy: 0.9772 - val_loss: 0.0241 - val_accuracy: 0.9940\n",
      "Epoch 7/20\n",
      "31368/31368 [==============================] - 71s 2ms/step - loss: 0.0641 - accuracy: 0.9804 - val_loss: 0.0201 - val_accuracy: 0.9955\n",
      "Epoch 8/20\n",
      "31368/31368 [==============================] - 72s 2ms/step - loss: 0.0554 - accuracy: 0.9828 - val_loss: 0.0198 - val_accuracy: 0.9958\n",
      "Epoch 9/20\n",
      "31368/31368 [==============================] - 72s 2ms/step - loss: 0.0497 - accuracy: 0.9850 - val_loss: 0.0161 - val_accuracy: 0.9962\n",
      "Epoch 10/20\n",
      "31368/31368 [==============================] - 79s 3ms/step - loss: 0.0490 - accuracy: 0.9854 - val_loss: 0.0224 - val_accuracy: 0.9949\n",
      "Epoch 11/20\n",
      "31368/31368 [==============================] - 80s 3ms/step - loss: 0.0514 - accuracy: 0.9850 - val_loss: 0.0156 - val_accuracy: 0.9962\n",
      "Epoch 12/20\n",
      "31368/31368 [==============================] - 77s 2ms/step - loss: 0.0433 - accuracy: 0.9869 - val_loss: 0.0186 - val_accuracy: 0.9959\n",
      "Epoch 13/20\n",
      "31368/31368 [==============================] - 76s 2ms/step - loss: 0.0442 - accuracy: 0.9872 - val_loss: 0.0174 - val_accuracy: 0.9952\n",
      "Epoch 14/20\n",
      "31368/31368 [==============================] - 78s 2ms/step - loss: 0.0400 - accuracy: 0.9878 - val_loss: 0.0164 - val_accuracy: 0.9959\n",
      "Epoch 15/20\n",
      "31368/31368 [==============================] - 80s 3ms/step - loss: 0.0385 - accuracy: 0.9877 - val_loss: 0.0165 - val_accuracy: 0.9966\n",
      "Epoch 16/20\n",
      "31368/31368 [==============================] - 86s 3ms/step - loss: 0.0400 - accuracy: 0.9884 - val_loss: 0.0123 - val_accuracy: 0.9976\n",
      "Epoch 17/20\n",
      "31368/31368 [==============================] - 82s 3ms/step - loss: 0.0372 - accuracy: 0.9887 - val_loss: 0.0126 - val_accuracy: 0.9960\n",
      "Epoch 18/20\n",
      "31368/31368 [==============================] - 80s 3ms/step - loss: 0.0344 - accuracy: 0.9905 - val_loss: 0.0124 - val_accuracy: 0.9971\n",
      "Epoch 19/20\n",
      "31368/31368 [==============================] - 82s 3ms/step - loss: 0.0421 - accuracy: 0.9884 - val_loss: 0.0126 - val_accuracy: 0.9972\n",
      "Epoch 20/20\n",
      "31368/31368 [==============================] - 78s 2ms/step - loss: 0.0286 - accuracy: 0.9910 - val_loss: 0.0264 - val_accuracy: 0.9949\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1ffad941048>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#using ten epochs for the training and saving the accuracy for each epoch\n",
    "epochs = 20\n",
    "history = model.fit(X_train, y_train, batch_size=32, epochs=epochs,\n",
    "validation_data=(X_val, y_val))\n",
    "\n",
    "#Display of the accuracy and the loss values\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(0)\n",
    "plt.plot(history.history['accuracy'], label='training accuracy')\n",
    "plt.plot(history.history['val_accuracy'], label='val accuracy')\n",
    "plt.title('Accuracy')\n",
    "plt.xlabel('epochs')\n",
    "plt.ylabel('accuracy')\n",
    "plt.legend()\n",
    "\n",
    "plt.figure(1)\n",
    "plt.plot(history.history['loss'], label='training loss')\n",
    "plt.plot(history.history['val_loss'], label='val loss')\n",
    "plt.title('Loss')\n",
    "plt.xlabel('epochs')\n",
    "plt.ylabel('loss')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Applying the model on test data\n",
    "Having trained the CNN model, the test data is used. The predicted decisions has been stored as \"y_pred\". As can be seen the accuracy of the model is about 0.97%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[16  1 38 ... 35  7 10]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9687252573238322"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Predicting with the test data\n",
    "y_test=pd.read_csv(\"C:/cmder/Python_Tests/GTSRB/Test.csv\")\n",
    "# labels=y_test['Path'].as_matrix()\n",
    "labels=y_test['Path'].to_numpy()\n",
    "y_test=y_test['ClassId'].values\n",
    "\n",
    "data=[]\n",
    "\n",
    "for f in labels:\n",
    "    image=cv2.imread('C:/cmder/Python_Tests/GTSRB/Test/'+f.replace('Test/', ''))\n",
    "    image_from_array = Image.fromarray(image, 'RGB')\n",
    "    size_image = image_from_array.resize((height, width))\n",
    "    data.append(np.array(size_image))\n",
    "\n",
    "X_test=np.array(data)\n",
    "X_test = X_test.astype('float32')/255 \n",
    "y_pred = model.predict_classes(X_test)\n",
    "\n",
    "#Accuracy with the test data\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "print(y_pred)\n",
    "\n",
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing the true labels with predicted labels and using the statistical parametric mapping (as the SafeML method)\n",
    "<p style='text-align: justify;'> \n",
    "The idea of SafeML is to measure statistical distances and estimate the accuracy of the model when there is no available labels. Having an accuracy estimation at run time can be so vital for safety-critical applications. In this section, the statistical difference is addressed and the accuracy estimation will be considered on our later versions. Here, we simply find the test data where y_test is not equal to the predicted one (y_pred) and name it as X_test_wrong, y_test_wrong. Then for example, for label == 2, we compare the trained data (trusted data) and the X_test_wrong (selecting only label 2 ones for instance). Then using statistical parametric mapping, we try to find the statistical explanation to see what was different that our CNN model made a wronge decision. This explanation can be investigated in different perspectives and we just provide a sample.</p>\n",
    "\n",
    "To find more about SafeML idea, please check our GitHub:\n",
    "\n",
    "https://github.com/ISorokos/SafeML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.2021911463245456\n",
      "0.9607438797885857\n",
      "0.7980090968897668\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "from matplotlib import pyplot\n",
    "import spm1d\n",
    "\n",
    "# Separating Wrong Responses of the CNN Classifier\n",
    "X_test_wrong, y_test_wrong = X_test[np.where(y_test != y_pred)], y_test[np.where(y_test != y_pred)]\n",
    "\n",
    "# print(X_test_wrong.shape)\n",
    "\n",
    "# Finding Wrong Decisions for Label 2 (just an example)\n",
    "X_test_wrong3, y_test_wrong3 = X_test_wrong[np.where(y_test_wrong == 3)], y_test_wrong[np.where(y_test_wrong == 3)]\n",
    "\n",
    "# print(X_test_wrong1.shape)\n",
    "\n",
    "X_train3 = X_train[np.where(y_train[:,3] == 1)]\n",
    "\n",
    "\n",
    "fig2, ax6 = pyplot.subplots(1,3, figsize = (20,6))\n",
    "\n",
    "for ii, c_ax in enumerate(ax6.flatten()):\n",
    "    # Comparing X_train for Label == 2 with X_Test_wronge for Label == 2\n",
    "    xxx, yyy= X_train3[:,:,:,ii], X_test_wrong3[:,:,:,ii]\n",
    "    xxx_2   = np.array([yy.flatten() for yy in xxx])\n",
    "    yyy_2   = np.array([yy.flatten() for yy in yyy]) \n",
    "    snpm    = spm1d.stats.nonparam.ttest2(xxx_2[:30], yyy_2[:30])\n",
    "    snpmi   = snpm.inference(0.05, two_tailed=True, iterations=1000) # Alpha is considered as 0.05\n",
    "    \n",
    "   # print(xxx_2.shape)\n",
    "    J,Q     = xxx_2.shape\n",
    "    z       = snpmi.z\n",
    "    zstar   = snpmi.zstar\n",
    "    z0      = np.zeros(Q)\n",
    "    z0      = z\n",
    "    Z0      = np.reshape(z0, (30,30))\n",
    "    Z0i     = Z0.copy()\n",
    "    Z0i[np.abs(Z0i)<zstar] = 0\n",
    "    ZZ      = np.hstack( [Z0, Z0i] )\n",
    "    \n",
    "    print(1 - z[z != 0].mean())\n",
    "    \n",
    "    c_ax.imshow(Z0, 'jet') # Can be replaced with Z0i\n",
    "    c_ax.set_title('RGB: {} of Set {} vs. Set {}'.format(ii, 1,2))\n",
    "    c_ax.axis('off')\n",
    "\n",
    "fig3, ax7 = pyplot.subplots(1,3, figsize = (20,6))    \n",
    "\n",
    "for ii, c_ax in enumerate(ax7.flatten()):\n",
    "    c_ax.imshow(X_train3[1,:,:,ii], interpolation = 'none')\n",
    "    c_ax.set_title('RGB: {} -- Test {}'.format(ii+1, 3))\n",
    "    c_ax.axis(\"off\")\n",
    "    \n",
    "fig4, ax8 = pyplot.subplots(1,3, figsize = (20,6))     \n",
    "\n",
    "for ii, c_ax in enumerate(ax8.flatten()):\n",
    "    c_ax.imshow(X_test_wrong3[1,:,:,ii], interpolation = 'none')\n",
    "    c_ax.set_title('RGB: {} -- Test {}'.format(ii+1, y_test_wrong3[1]))\n",
    "    c_ax.axis('off')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Some Draft Codes\n",
    "Here is some draf codes and it should be ignored."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\spm1d\\stats\\_clusters.py:191: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
      "  self.P             = (pdf >= self.metric_value).mean()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "900\n",
      "900\n",
      "900\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 2160x864 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 2160x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABpgAAAIKCAYAAAA3RkwDAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAABK4ElEQVR4nO3debhlZ1kn7GedfeYaTs2pqswjYQrIGEDCDCKKA2ArKKIiIiraOLW0frRDK42CKIojLSCCDKIoSpgJM2E0kJCBzFNVqlLzOXWmvdf3RxXdfP0lRRa/jadC3/d1eV0mm99bz957rXc9az3nVJq2bQsAAAAAAADuqpGVLgAAAAAAAIC7FwMmAAAAAAAAOjFgAgAAAAAAoBMDJgAAAAAAADoxYAIAAAAAAKATAyYAAAAAAAA6MWACAAAAAACgEwMmCDRNc13TNIebpjnUNM2Opmle2zTN6v/jf/Ogpmne2TTN3qZp9jVNc1nTNP+9aZr1R19/TtM0/aNrHGqa5pqmaX66Qw3jTdO87WgtbdM0jx7i+3vk19Q1e3T9Q1/zf6d8A2u2TdOcdYzXtzVN889N09xy9H97WvQmAIDjxnHSO53fNM17m6bZ0zTNrqZp3to0zbYhvT+9EwAwFMdJ33Svpmk+c3T9vU3TvK9pmnsN6f3pm+BbgAET5L67bdvVVXX/qvq2qvq1r77QNM3Dq+pDVfWxqjq3bdt1VfUdVbVcVff7mjU+0bbt6qPrPL2qXtY0zbd1qOGjVfXDVbXjG38b/39t237ka+q699F/ve6r/65t2xuG+ecdNaiqC6vqad+EtQGAlbfSvdP6qvrLqjqtqk6tqoNV9Tff+Nv53/ROAMCQrXTfdMvRzIaq2lRV/1xVfx+8n/9F3wTfGgyYYEjatt1RVe+uIxf9r3pZVf1N27a/17btzqP/uxvatn1J27YfupN1PldVX66qe97FP3exbdtXtm370arqB2+hk6ZpZpqmeU3TNLc2TXNz0zS/0zRN7+hrZzVNc1HTNPubptndNM2bj/77Dx+N//vRn0b5T3fwfna2bfvqqvr0f9R7AQD+461g7/Sutm3f2rbtgbZt56rqT6rqEd/4O7lr9E4AwDdqBfumfW3bXte2bVtVTR157nSnvyE0LPomuPswYIIhaZrmpKp6clV95eg/r6qqh1XVP3Rc58FVdU5VfeZr/t0lTdM8c3jVDsXr6shPxZxVR36K5olV9dyjr/12Vb2njvyE8ElV9aqqqrZtLzj6+v2O/jTKm/9DKwYAjhvHUe90QVVd2uXP/AbpnQCAb8hK901N0+yrqvk60qP8bpc/8xukb4K7CQMmyP1T0zQHq+rGqrqtql5y9N+vryPn2P/6a+uapnnZ0b8Td7Zpml//mjXOP/rvD1XVxVX1t1V11VdfbNv2vLZt3/jNfiN3VdM0J9SRxuYX2radbdv2tqr6w6r6waP/k6U68lfObG/bdv7ob1cBAFQdR71T0zTnVdX/U1W/nL6pr/Pn6J0AgG/EcdE3Hf3r92aq6mer6vP527pz+ia4ezFggtz3tm27pqoeXVXn1pG/k7aqam8d+btd/9d/NLpt2185elH+x6oa/Zo1Ptm27bqjf+/s1jryd88O/SdC/o//gOKlR//dpV/z7x55F5c6tarGqurWo03Kvqr6i6racvT1X6kjvzp98dH1f3zIbwUAuPs6Lnqno/8B6HdV1c+3bfuRO/nf6J0AgJV0XPRNR9efrao/r6rXN02z5f98Xd8E/3cyYIIhadv2oqp6bVX9wdF/nq2qT1XV93dcZ2cd+RXn7x5yif+f/4Bi27b3Pvrv7v01/+4OH67cgRuraqGqNh1tUta1bbv2a9bc0bbtT7Ztu72qfqqqXn30IQ4AQFWtbO/UNM2pVfW+qvrttm3/9hhr650AgBV3HD1zGqmq6ao68Q7W1jfB/4UMmGC4XllVT2ia5v5H//lXqurHm6b5L1/96Y6jf2/u6Xe2QNM0G6vq+6rDfwugaZqJpmkmj/7jeNM0k03TNN9A/XdJ27a31pG/7/blTdOsbZpmpGmaM5umedTRep5x9H1WHfmpmraO/Icgq6p2VtUZx1r/6HuZOPqPX/veAIBvLa+s/+DeqWmaE6vqA1X1p23b/vk3Xvpdp3cCAIbglfUf3zc9oWmab2uaptc0zdqqekUd6VW+/A2/i69D3wR3LwZMMERt2+6qqtdX1W8c/eePVtVj68h/PPrKo7/We2FVfaiO/kcIj3rYV39luI5cpHdV1c999cWjv/L7rGP80VdU1eE68hMk7z76/586nHd1p55dVeNVdVkduaC/rf73r2Y/uKo+dfT9/HMd+atnrj362n+rqtcd/TXnH7iTtQ9X1aGj///lR/8ZAPgWs0K903PryIOHl3zNX9ly6E7+t8OkdwIAvmEr1Detq6o3VdX+qrq6qs6qqu9o23Z+aG/sjumb4G6iadt2pWsAAAAAAADgbsRvMAEAAAAAANCJARMAAAAAAACdGDABAAAAAADQiQETAAAAAAAAnYwe68XvOOdX2mTxdnIiiR/Ra6J4M7cQl9AcztZYPmljXEOq7WWzxLEbdudFLC1l+SY7Fqqq2lVTWQlLy3ENsV4vyw8GcQntwUPZAutn4hqa5X6Ub/cfjGuoNvwsm3DGPz6W5auqSffpYZyXs4ezEoZwrWnn5rIFBtHl8oh+dkxfuO81+ZdB7Enn/UbWO03l5/XyqmyN/kT+80eTO2aj/MLm6biG/mR2vVyYyT6Hmauyz6CqqndwPsqnPWxVVfXDa13Yg1ZVtaPZdxn3DMO43i5mfXB/8xB6p4Wshubm2+Ia2sPZMZ32Hc10vrfUujVRvJ0aj0sYOZR9ju3IEFqG2/dlNSwMYX8Kj4d3H3yt3uk4cP4zXx71TpN78nv0uS3HfDT2dQ1G80Np/GB2ve0t5PcjC+uya/bhTVl+/ZXhM6OqGt+frdGbXYxrGNmbPWtoVw/hWpUKe6ehPHeazu7z27Hw2VlV9SezvWHstiE8d9qzL8u32d7QrM36nqqqQfgsdjCd9+Lp8dDbH/awVTWy90CUb+eyZ2dVVTWaHdMX7nz1HV7w/AYTAAAAAAAAnRgwAQAAAAAA0IkBEwAAAAAAAJ0YMAEAAAAAANCJARMAAAAAAACdGDABAAAAAADQiQETAAAAAAAAnRgwAQAAAAAA0IkBEwAAAAAAAJ0YMAEAAAAAANCJARMAAAAAAACdGDABAAAAAADQiQETAAAAAAAAnRgwAQAAAAAA0IkBEwAAAAAAAJ2MHuvFwZrpaPH+2vEoX1XVm1uO8s38UlxDO5m9j97BhbiGWso+h8GqySi/vG19lK+qGlnsZ/m9B+MamuWshnZ2Lq6h1q2N4tf+3uoo/7jTrozyVVUXveWBUf6k9+6La2j2z2YL9IYwX++NZfl+djw2kxPZn19V7aqpLD/Wi2sYCfe3oQi/ixq0eQ1jx7wkczfRTmbf49yJ2TlZVTWylB2P43sX4xra0WyPHT0cnpNVNb4/ex8jS9keu7Ap672qqsbDPXbs1rwPjg1hj2/C/GW/viXKP+JeXwkrqPrMe+8V5bd9Iv8uJ3Zne0PTpN9EVTMVnhfh9bZZnd3fVlX1162K8ktDuEceH2T5ZiHf52s864PbQ2EvX1XNMPp5VtzYoeyAPnhieE9WVYvrsv1tYm9+L7C4Jjuepxby3mlqd7ZGL9xaZrfm90ML67LeafXN+bVubG4IzwBDg6nsvLjyZ7PnTueeemuUr6q6dld2vV33jixfVTW5Jzsnxm8aQi++KuxdFrMa2iFca/szWf938JT8vmpif/ZdTobPtKuq2oXsfbQH8ufitTiEHvAO6MgAAAAAAADoxIAJAAAAAACATgyYAAAAAAAA6MSACQAAAAAAgE4MmAAAAAAAAOjEgAkAAAAAAIBODJgAAAAAAADoxIAJAAAAAACATgyYAAAAAAAA6MSACQAAAAAAgE4MmAAAAAAAAOjEgAkAAAAAAIBODJgAAAAAAADoxIAJAAAAAACATgyYAAAAAAAA6MSACQAAAAAAgE5Gj/Vi0+9ni99+OMpXVbVjvSg/WDuV19Bk+dHdB+MaanEpiveWlqN8f+OaKF9VtTwzEeXHDi/GNQymsxoG29fHNdz02FVR/pKHvyrKjzXZOVVV9e2POzHKz165Ka5h1bWDKN9Mjsc1NHsPRPm2WfkZfzOfnVfNwWxvqaqqiey7aA8MYY8dPebl8OsL9+iqqnYIa7Dylldnx/Pooaz3OlJDts8vrsv3x3Yku95O7p6PaxjZeyjKTx3Kalg8Ie+dFtaHx9O+7HuoquqvympYXJ/XcPu9sxo+/aTfj/KbelnvVlX1wqfMRvmL9j44rmHrJxeifP+M7XENvT3ZeVn7wt6rl/dezUJ2vR7fOYTrfS+8Qd03hN5pKbw3a7NevqqqXc7XYOUtrsnOy4mDQziWRrIaBuGtRFVVfyI7r3sL+f62+sas9+nNZ33s0lTeM8xuzT6Hydvz5yVLZ26I8vvPGItr2H9u9l1c/LhXRPlh9E6XnJ4dj9879zNxDePXZ8fk6bfkn0OzlH2XTT/cI3v5OdGfyNZYfUvWw1ZV9ceyvWFkX9bLV1U1B7M12sEweqchPMO7Ayv/dBMAAAAAAIC7FQMmAAAAAAAAOjFgAgAAAAAAoBMDJgAAAAAAADoxYAIAAAAAAKATAyYAAAAAAAA6MWACAAAAAACgEwMmAAAAAAAAOjFgAgAAAAAAoBMDJgAAAAAAADoxYAIAAAAAAKATAyYAAAAAAAA6MWACAAAAAACgEwMmAAAAAAAAOjFgAgAAAAAAoJPRY73YXzMZLT4ytxTlq6pGFpezBRbauIZqszXayfG8hpEmijeD7D30btoV5auq6qTNUXzHY7N8VdUZz7wqyj9768fjGk4c3RflL1nM/vx9g/x4fMU93hzlx185iGu4YXl9lP/rWy6Ia9jzqtOi/Mynbo7y7f4DUb6qqplZmy2wHO7Rw1ijnx9P1e9H8WbVdFxCu3FdvAYrb37TWJQfO5QfzxO3h/1X2PdUVbVj2c8wDcZ6cQ0jkxPxGonx2w7Fa/Qnsj36qmdviGt4xhM/FuUvWHNFXMPZY7dH+V397Hi8Yik/L//z5g9G+V96UZavqvr0/PYo/4+7HxDXcNnr7xnlt34w2xua2cNR/sgi4X3ZfNjMV1UNsmNyMD8fl9AuZu9jZM2auIbasjFfgxW3/4xsj565Nt+jR8PnRmNzee/UH8v2lv5Elq8aQv/Wy2qYuSbfm8bmsucd1z8rP56ee/+sd/q+tZ+Pa7jneHZ/2m+novxSm91fV1Xdeyz7Lq954mviGhba7L7qaY96alzDFRefFuVP/8fsuxzdMxvlq6p689kzn9Hd+X3V+ELWtwzj+dtgMTuemunsu6yqqpNOyNe4A36DCQAAAAAAgE4MmAAAAAAAAOjEgAkAAAAAAIBODJgAAAAAAADoxIAJAAAAAACATgyYAAAAAAAA6MSACQAAAAAAgE4MmAAAAAAAAOjEgAkAAAAAAIBODJgAAAAAAADoxIAJAAAAAACATgyYAAAAAAAA6MSACQAAAAAAgE4MmAAAAAAAAOjEgAkAAAAAAIBODJgAAAAAAADoZPRYL47dui9afDA9GeWrqmppOYo3c/NxCe3aVVF+MDUW19CMZLPAtIad37k9yldVPf7HPxHlN40dimv49wMnRflfeeNz4hrWXTWI8r3F7M8fWW6zBapqaaqJ8nNb89n29ONui/LPP+PDcQ3nvuxfo/wLv/yDUX7mpZujfFXVyEc+H+V7m/Maajnb52skOx6rqqqX7ZHtujVxCYPp/FrBylt9dXatGllYyovoZ9eZGoT5qhrMTEf5hY15D9mOZjWM3T4X5a95xqYoX1X18mf9zyi/r5/1sFVVH95/TpT/mXf+WFzDqhuzvqF3OPvzh9E7LYe908LGvIY1D7g9yv/yOe+Oa/j9F78zyr/4OU+O8pf8zX2ifFXVljdcEuXzb7Kq2nCVJu+dRqayfbqZyXunNv0cOC5M78y+x+mdee/U9rJzYmQp752WVh3z8dzXtTzVi2vYc4+JKL/uK9nDihsfPxXlq6pe+szXR/mnTO+PaxhUdjx8aTE7Fqqq3jOX3VuuGlmI8pNNP8pXVR0cZNeZNSP58+CzxrL38Y6zs2dGVVW9c7I++Bcf94Ao/44PPSTKV1Wd9abwee6efXEN7WJ2rRhGz9FMZ3tcs3YIz53C692d8RtMAAAAAAAAdGLABAAAAAAAQCcGTAAAAAAAAHRiwAQAAAAAAEAnBkwAAAAAAAB0YsAEAAAAAABAJwZMAAAAAAAAdGLABAAAAAAAQCcGTAAAAAAAAHRiwAQAAAAAAEAnBkwAAAAAAAB0YsAEAAAAAABAJwZMAAAAAAAAdGLABAAAAAAAQCcGTAAAAAAAAHTStG17py9+xz1/7c5fvCuLzy8k8aqqGsysivLtWC+uob96PMo3S4O4hsMnTEb5zf/5mij/0lP/KcpXVX33J386ym//m4m4hrH9i1F+ZKkf11D96LSqZpAdT4OJ0ShfVVUjTRRvlvNzYhCe220vew9VVdc/ZSrK/+Ez/ibKL7X5d/mKX3pmlJ96x8VxDb3Nm6N80xvCz0r0wmtFP98b2rnDUf7Cfa/JD2pij3/E70Sb/Ngte+Ia2umsZ1jcuiauYXEm25/aJj+ce4vZteb678n+/E89+ZXZAlX1X295YpT/3GvOi2tYfWu2vzVh31NVVeESTZjvTwxhew2XaJbzEtIaZk/I76tuP38pyr/g/A9G+R+buSTKV1U94vW/FOXPeOmX4hrSvqMZH4tLaFavjvLt/Hxcw2Df/ij/nsU36Z2OAw987iuiXXr9l+fiGtrR7H7i0Mn5s4qlqfBwHMItUXq9O+H7r4/ybzj7LVG+qmq6yfa3K5fyvmV6JLtonzCE+9teeNFfarM+up82b1U11mSfw1jlfcueQfYM8eAg/y63j2bf5XSTPdMea/LP8TGXZjdWEy9eG9cwctUN8RqpZt1MtsDyEJ47HQ6fO+36izs8IP0GEwAAAAAAAJ0YMAEAAAAAANCJARMAAAAAAACdGDABAAAAAADQiQETAAAAAAAAnRgwAQAAAAAA0IkBEwAAAAAAAJ0YMAEAAAAAANCJARMAAAAAAACdGDABAAAAAADQiQETAAAAAAAAnRgwAQAAAAAA0IkBEwAAAAAAAJ0YMAEAAAAAANCJARMAAAAAAACdGDABAAAAAADQyegxX+1l86d2YjzKV1Utr5+K10j1Di1G+eU1E3ENJ7zo6ij/3G0fifLPeMUvR/mqqjPftTPKD9b04xpqkMX33XtNXMLs9uy8WlzTRvn+dJavqhrfl72H6VvyGjZdcijKN4fz4+mMty1F+d+4/sei/E+88J1RvqrqD1/5J1H+Z6dfGNew7p+/mC2wbUtcQ3NoLsq3C9l1oqqqer18DVbcyPxylG+nJ+MaltdNR/nFmWO3h3dFfzy7Tqy6eT6u4SvPy2p40YPeH+Uf+i//OcpXVW27KHsPaw5kx2NVVdtrovyhbfnxNLctq2FhU9YADqbznmF0X/Y5TNyefQZVVTPXZO9j9S358bTqn7Jj+q0ffWKU/9iPnRXlq6oue86fRvl7jvxMXMNZf3BFlG/W5vc0tRRe7/ROHJXuLUsz+XOnNjyU2mH86Ha4xuSe8GFHVc396L4of+G5/xrl9w/yD/LWfra3nDU2hD44/DL39PO+42Cbvo/sPYylD9+qaqmy3qdX+ee4ITwktw7hMrXQZp/lTcvZObG5l3+XH7z3O6L8I377++Ma1vzaSVG+t/dgXEM7Gh4Qh/N75Grye4o74jeYAAAAAAAA6MSACQAAAAAAgE4MmAAAAAAAAOjEgAkAAAAAAIBODJgAAAAAAADoxIAJAAAAAACATgyYAAAAAAAA6MSACQAAAAAAgE4MmAAAAAAAAOjEgAkAAAAAAIBODJgAAAAAAADoxIAJAAAAAACATgyYAAAAAAAA6MSACQAAAAAAgE4MmAAAAAAAAOhk9FgvNgdmw9V7Wb6qxq+/PVtguR/X0B46FOVn37QpruHXt38gyv/yf/+pKH/iJ3dH+aqqapoovv8ea+ISdj5pMcq/8uGvj2t4ynR2PPWau/9c+OKFpXiNH//8c6L8yMfWxTWc9M7bovyWTx+I8n/7u0+J8lVV173oU1H+p1/ytriGv7v2O6P8yKHsvK6qqonxLL9jV15DP79esfJ6+7I9vh0fi2sYPTAf5VeH+aqqhRNWR/mbX5RfJ976gL+J8s/625+P8qd+YjnKV1W1o4Mof2j7MVv9u7bGk7Jj+uUPeGtcw2Mms+vl9Ei4xx8HdvfD+7Kqet613xvlv/DFM+Iatl2U3Q9M7s2ulTv+6vQoX1X1HT/2PVH+j5/xP+MafvvzPxbl11x9MK6hWQr7lj174xr0Tt8aJnbNRfnBRH6ta3vZPfbkzsNxDf3J7H1c8/TJuIarH/TmKH/rctYzDOOM3tTLnkPu6uf9W9a9VZ05lvXRDM9tYf+1YwgH9WTTRvnTw2fzN/XTI7qqKvsc33ufv48reMD3Zfd2J16U77ETO7I9sm4LZyRVVeEeeWfu/k+qAQAAAAAA+A9lwAQAAAAAAEAnBkwAAAAAAAB0YsAEAAAAAABAJwZMAAAAAAAAdGLABAAAAAAAQCcGTAAAAAAAAHRiwAQAAAAAAEAnBkwAAAAAAAB0YsAEAAAAAABAJwZMAAAAAAAAdGLABAAAAAAAQCcGTAAAAAAAAHRiwAQAAAAAAEAnBkwAAAAAAAB0MnrMV8fHosXbsWMvf5fWmMxqGLltb1zDbU+/V5R/3mnviGv4zy9/fpTf/v4bo/xgZlWUr6q64Xs3RfnnP+tf4xp+bv318Ro5c92HTGTndVXVl87/uyh/8bctxTX8yIYXRvkz3rIvyq+7/FCUr6r6yCseGuVPe/HuuIYd/yX7LrY/K9vfqqoqvV41zmuO6K9bHeXbsfxYGoz3ovzYjv1xDbu+bSLKf+78P49rOPc9Px3lT/v4cpRfns6/y5ue0o/yLzr/3XENx0fvNL7SBay4Tb28F3/7We/NFjgrLqEuOOv7ovzCG7ZG+fFDgyhfVbX77SdH+X0vnI5rOP0XLo/ye35ic1xDe/3N2QKD/LtoRvPnDay8xfWTUb4/mfU9VVXLU02UX3VTfjzf9qBsb/jc018e13DDctZ3pIZxR7XQZt/FKaP5Ht1zb/gtY0vYf23Jt6e6aTl77nNTP3ze0svfxJ5Bdl+1pZffC7z8mX8T5V928bPjGiZu25MtEM5pquqb9uzKrgcAAAAAAEAnBkwAAAAAAAB0YsAEAAAAAABAJwZMAAAAAAAAdGLABAAAAAAAQCcGTAAAAAAAAHRiwAQAAAAAAEAnBkwAAAAAAAB0YsAEAAAAAABAJwZMAAAAAAAAdGLABAAAAAAAQCcGTAAAAAAAAHRiwAQAAAAAAEAnBkwAAAAAAAB0YsAEAAAAAABAJwZMAAAAAAAAdDJ6rBeXtq2LFh+ZW4ryVVXNYBDlF87dHtfw/S/8QJT/vYu+K67hXhfeEuUHM6ui/A3ftSHKV1W95rmvivLnT/biGuCrHjIxFq/xkWf/QZQ/f+2Lovy5r94T5auq1n25jfKveM9T4ho++P3Z5/jDT8g+x6qqVe//cpRvxoewP23YmK/Bips9bXWUn9yzGNewHF4vl8/aFNfwwGd8Mco/7LM/HNdw6tuyn6MajDVR/pZHZvmqqnc//o+i/DljWf8Hw/bh+/5jlP/B5z82yl/9mntE+aqq6V3Z/emvfeRpcQ0feeIro/x3PeFX4hq2/fXN2QK9vHdqTtwar8HK2/WAiSg/uTu7n6mqWlybXbP7E1NxDad/39VR/uCgH9ewFH6UG0ay3qtf+Xe5qaf34VvLSaPZ/eVt/dkof0s/31s2jGR77JVL2Xuoqnr8VDaj+IUL8r7l7CvXRvnmYP45DE7In+/fEb/BBAAAAAAAQCcGTAAAAAAAAHRiwAQAAAAAAEAnBkwAAAAAAAB0YsAEAAAAAABAJwZMAAAAAAAAdGLABAAAAAAAQCcGTAAAAAAAAHRiwAQAAAAAAEAnBkwAAAAAAAB0YsAEAAAAAABAJwZMAAAAAAAAdGLABAAAAAAAQCcGTAAAAAAAAHRiwAQAAAAAAEAno8d8ddBGiw+mjr38XbE4Mx7lb3/ebFzDoJoof+J7s3xV1WDtdJS/6QnrovxvPecNUb6q6vzJXpS/ZHE+ruFVOx8X5Z+x8eK4hidOL8VrrLSFNnsPL7z5griGD159TpRft3YuruF1935dlP/w97w8yj96/pejfFXVWW85FOVPev8gruEl5z85ys/+xL64htUfm4zyTS//eY32QPZdcHwYhK3P/Mas76mqWlibHY/7n5z3Tv/jhA9E+Z9428/HNTT97Fp14JSsb3n+E94T5auqzhlbFeWvXsr3lbcfvF+UP3tiZ1zDk6f3RvmJZiyuITU3WIzyP3/zY+IaLrrmrCi/dcOBuIa33yu7p/j707O95bynnhDlq6rG3zgT5Td9PD8eX/+QB0b5jU+9Ka5h5B83RPn28OG4hmYuvz9k5Q2yy23Nb8qft/TD9mv9d+6Ia/ins98d5b+wkO8tJ40uR/k9g+ze8IRe/gwxdety3ju949A9ovxp47vjGh43lT3vGGvCE3MIltp+lH/LoS1xDe+6/b5Rfm45Py9feuo/Rfn0fqLf5ufE/vCx0Zp8m4+Pp9c8/c/iGv7rJ34qyq/9ZH6fPrL3YLzGHa77TVkVAAAAAACAb1kGTAAAAAAAAHRiwAQAAAAAAEAnBkwAAAAAAAB0YsAEAAAAAABAJwZMAAAAAAAAdGLABAAAAAAAQCcGTAAAAAAAAHRiwAQAAAAAAEAnBkwAAAAAAAB0YsAEAAAAAABAJwZMAAAAAAAAdGLABAAAAAAAQCcGTAAAAAAAAHRiwAQAAAAAAEAnBkwAAAAAAAB0MnqsF/vTx3z561pYn+WrqtqRJsr/xDkfj2v4q8sfEeVPvWRXXMPSCWuj/P2+77Io/7TVB6J8VdVP3fSwKP+BD90/rmF5bT/Kv3/hvnEND3/w5VH+Dad9KK4h9ZM3PC7Kf/Sqs+IafuR+n4ry77/1HnENT//sT0b5Sx/2d1H+UY/8YpSvqrrx7WdG+ekbD8U1fOwD94nyL3naW+Ia3jj64HiN1GDbppUugSHoLbZR/sApee/Un8zyv3je++Ia/mLnY6L8hiuW4xr6k1kPedIPXhvlf3nD1VG+quo1+7dG+d/52HfFNVT2MVZzKD+m/+w+N0X5d577jig/1vSifFXVz9yU9U4XXXyvuIbHPOTSKD86kvXRVVXfe+mPRPm33uv1Uf7X7/VvUb6q6ndOelaUX33zIK7hrz+f3Z++9YI/j2v4tZOzPnj0yzfENbRTE/EarLw23GIPnb4U1zC6J7tWPXlbtr9WVV26eDjKzwxhj94zyC76a8KeYfVI2MRW1WcXFqP8D3zs5+IaJiazY7Jtww+yqh5z2lVR/lXbs2epvSb/fYaPzY9F+VdelfVeVVU/c9aHovz8IHsPVVU/deUzo/ybz31jlJ8ZGY/yVVVLg+y8HGvyc+L65ew+/ezR/NnXwtrwvJjIv4vB6ul4jTviN5gAAAAAAADoxIAJAAAAAACATgyYAAAAAAAA6MSACQAAAAAAgE4MmAAAAAAAAOjEgAkAAAAAAIBODJgAAAAAAADoxIAJAAAAAACATgyYAAAAAAAA6MSACQAAAAAAgE4MmAAAAAAAAOjEgAkAAAAAAIBODJgAAAAAAADoxIAJAAAAAACATgyYAAAAAAAA6GT0WC+O75qNFh9ZnIryVVW7z8vWePj0VXENr77yyVG+nerHNdzyyOko//vb3h3lb1peivJVVe+78two/5THfiau4eXbPhnlX3DTBXENH77uzCg/d8pilH/P4Q1RvqrqI1+8R5T/H49+S1zDD6zeH+V/c/OlcQ3PvPYxUf79h3tR/kVb3xvlq6p+4DHnRfmT3ncwrmHsUBPl5wdjcQ2DLeuj/Mhte+MaRg7OxWuw8iZ3LUT5kcU2rmHvOdk58Yipq+Mafu/DT4nypw7yz+HAydnn8JKTsj32tn7WR1dV/cU1j4zyL3pYfp143rqvRPk3HTwxruG3PvVdUf7zZwyi/EMmsut1VdVnd5wU5X/0UR+Ja3jJ5sviNVIP3PkDUf5tB+8d5X9u/fVRvqrqV++d9eKrbz7mLfhdMnnlZJRf9+jsPVRV3fyoVVH+tBuy+9uqqrbNrxWsvHVXZXv01K78nDq8ObsfefKaL8Y1HByMR/kNo4fjGgbhObWhNxHl9w/y9/CB2ftE+R++78VxDS/e9IUof/1yvkf/+Jd/JMpfvjm7p7n3eP48+Obl7B799fd5bVzDPcfza1VqTW8+yn/o8PYonz57q6qqQXZMHxzCveFkkz2b39Qbwozjgdn1bsNlM3EN3yx+gwkAAAAAAIBODJgAAAAAAADoxIAJAAAAAACATgyYAAAAAAAA6MSACQAAAAAAgE4MmAAAAAAAAOjEgAkAAAAAAIBODJgAAAAAAADoxIAJAAAAAACATgyYAAAAAAAA6MSACQAAAAAAgE4MmAAAAAAAAOjEgAkAAAAAAIBODJgAAAAAAADoxIAJAAAAAACATgyYAAAAAAAA6GT0WC82S/1o8bHbDkb5qqrZkyaj/Mfnzo5rOOHTgyjfjvXiGhbuOxfl7z8xkf35bT6L/OAFr4ryp4yujmuoyr6LX976nriCj97w/Cj/paUmyt+4uDHKV1X1Vi9F+SdN74hrqJoawhqZ06Zvj/I3LmXfxeOmsj26qmrNt9+WLfC+/HsYWczyS22+x+6790yU33D7gbiGdjrbpzk+NMtZzzB1Y947Hd68PsqvGcn3lokdY+EK2XWmqmrfectR/tsn56P8SGU9bFXVO897bZTf0lsV11CVfZffs+q6uIKXrcouFAcH6XeRnxPLy9m16n7TN8Q1HA/OXJ/1Tgf7+XmVut9ZN0b5HR84Pa5hancb5T85f2pcw8TDsu+y/afpuIbldfkarLzeYnY8T+zPa1hand1jb+3l14ldlX0O81m8qqqms4+hJpqsZxgNn9dUVb1w/eVRPn0PR2RrnDmafw5bV2X3pwcH43ENqZsWN0T5c8dvHVIlK2vr6L4o//e7z4/y3zX9wShfVTXdZJvLrkG4OVXVupHsPn2pzff5pz7is1H+kxc/KK6htzCEi8Ud8BtMAAAAAAAAdGLABAAAAAAAQCcGTAAAAAAAAHRiwAQAAAAAAEAnBkwAAAAAAAB0YsAEAAAAAABAJwZMAAAAAAAAdGLABAAAAAAAQCcGTAAAAAAAAHRiwAQAAAAAAEAnBkwAAAAAAAB0YsAEAAAAAABAJwZMAAAAAAAAdGLABAAAAAAAQCcGTAAAAAAAAHQyesxXB220eDM3H+WrqpozZqP8V+ZOiGtYdeNclG/HenENo6P9eI3ERDMWr3HKaL5G6tLFw1H+F67+wbiGxfljn3Zfz73GsmPh8l52TlVV9XqDKL9/kB/PM8fBePzWhZkov218/5Aq+cY9ZMv1Uf7LU/eJa5jck11rbl1aF9cwuzU7oDb08gNy5GC2P3F8GL11b5RvFxfjGtpmfZRfyk7JqqrqLeRrpKY3Z9e7YfQ+qS29VStdQl25lH2OL9/5+LiGw/smo/wZo+n1dnWYr1pezq4Tty/nNVQdGsIamfl+dl4ttfl9VerRm66I8m+YOiOuYSS8VFy7sDmu4cChqSi/bT67XlZV9Q6u/PFAbvVV2R7dLC3HNRzesCXKTzT5vcB0k90jL1UT19CrITSByZ8/hM+xdxz8HP3+QXZf95f78nvsy3ZujfInnZrem+Z9y6bRg1F+vs2evR0vxsK94SsHNkX5ue1LUb6qqhfuTwtD6P+WKnuOee1y/hzzBZsuivIfWfXguIZ1V+TPhO/Iyu+8AAAAAAAA3K0YMAEAAAAAANCJARMAAAAAAACdGDABAAAAAADQiQETAAAAAAAAnRgwAQAAAAAA0IkBEwAAAAAAAJ0YMAEAAAAAANCJARMAAAAAAACdGDABAAAAAADQiQETAAAAAAAAnRgwAQAAAAAA0IkBEwAAAAAAAJ0YMAEAAAAAANCJARMAAAAAAACdjB7z1dv3Rou3y8tRvqpqfHxtlN8xvyauYWRuMcovr52Ma6hqh7AGv3rd90f5W953clzD1kftiPKrR7Lj6cSx7Lyuqhofz87tHf2JuIZTjr173S0c7A9jb8hsHJuN8m3TxDWMzWX726UHtsU1tOHx1K6ejmto9uyP12DltfsPZPnFrOeoqhqEx/PN/dVxDb2FeInYELYnqup3bnlylP/kh+4d17D5vN1R/vSx/JhOrZrKzu2ltjekSlbWZG8pyi+kG9wQbA976XYYP+IZ3hpOjGTfQ1XV6Gg/yi+fMBPX0Lv9ULwGK29kb9Y71WCQ19DfHOV7tfJNR78dQg2N507DcNHhjVH+9Vc9NK6h18vOi5NGV753OjjInpfsWF43hCrC/WkI0h7w0EL+/G2l9Yewx46l+Sa/1qwLe8D5DfnnMDL7zblR9xtMAAAAAAAAdGLABAAAAAAAQCcGTAAAAAAAAHRiwAQAAAAAAEAnBkwAAAAAAAB0YsAEAAAAAABAJwZMAAAAAAAAdGLABAAAAAAAQCcGTAAAAAAAAHRiwAQAAAAAAEAnBkwAAAAAAAB0YsAEAAAAAABAJwZMAAAAAAAAdGLABAAAAAAAQCcGTAAAAAAAAHRiwAQAAAAAAEAno8d6sZmaylZfWsryVXVo73SUnzxhOa9hejzKjyz14xoGA7PAYXjLWe+I8m844bS4hpd+6slR/v1n96L8Pcb2R/mqqpmp+Si/Y3kmrqEm5vI1Qqt6i1H+2sObwgquCPNVV89lNTTLg7iGwxuy/e3U6T1xDdfvbqP83Clr4xqmmiZeg5XXnrY9yo/sORjXMH4oO57XNgtxDYNjdphfX5O3TtXv652G4a9OeX+U//x/+mBcw3M+85wo/8+z2f3EU1flPce2tQei/Jpe1nsdLzZNzEb5WxeG0EOGvjB7apQfxv42GMvXSK2ayq4V+87ZGNew9vrsvojjw8I5W6P86L68b2nCx0bzbX5iL1V2L9Brsv6vqqqfL0HlfcOjHvyauIYfuuppUf7FO8+L8r97wiVRvqpqXS/7HOfb4+BieRyYW1j5z2EkfNYxOYzmKTRW+Qb5L4fOivLLq/Ma5k7/5vTS7rwBAAAAAADoxIAJAAAAAACATgyYAAAAAAAA6MSACQAAAAAAgE4MmAAAAAAAAOjEgAkAAAAAAIBODJgAAAAAAADoxIAJAAAAAACATgyYAAAAAAAA6MSACQAAAAAAgE4MmAAAAAAAAOjEgAkAAAAAAIBODJgAAAAAAADoxIAJAAAAAACATgyYAAAAAAAA6GT0WC+2E2PZ6ouLWb6qpq8aj/KPedjlcQ2vPekeUX7VdYfiGgYDs8BhmB7JjqfnzdwS1/DO02+O8q/ZcUGUf9nJ/xLlq6q2r94f5a9Y2BbXUKuuztcIbRzPzu1di2ui/FLbj/JVVZ+87vQof9qgjWuY35zlH7U23+e/eMV5UX70K/ne0ExNxmuw8pr5pSjfLmX5qqrVN81H+Ykm31va46BtWbh1OsofGmSf4+qRb41zeqLJ7gfOH8LHcPLGfVH+tbc+Iso/9az3Rvmqqj8/8y1Rfjin1OqhrJLot02UH6m870h99LYzo3wv3+Zrblv2OT50Ou+j/+LyJ0b5cz61K66hHTvm4wzuJnqz2UkxspCfVDPXZs+urliaims4dXQuyg/iCqrCJ4AMycxIfjw9e/snovxLvvDdUf63t3whyldVPX31DVH+4GA5ruF46J2W2rv/tW5ukN1fjjVZ31NV1QvXOKWXn5d/cuWjo/z2D+fXu9H5/F7/jhwHt/8AAAAAAADcnRgwAQAAAAAA0IkBEwAAAAAAAJ0YMAEAAAAAANCJARMAAAAAAACdGDABAAAAAADQiQETAAAAAAAAnRgwAQAAAAAA0IkBEwAAAAAAAJ0YMAEAAAAAANCJARMAAAAAAACdGDABAAAAAADQiQETAAAAAAAAnRgwAQAAAAAA0IkBEwAAAAAAAJ0YMAEAAAAAANDJ6LFebA4vRIu3UfqI7R89HOXX/dhcXMOtD8/mcGdf3Y9rmLxkJsp/6KHZe3j01CDKV1W99sCWKL/YHvNwvUueN3NLlD80mI9r+OKXT4ny9zjn5ij/qtu/PcpXVX36ytOj/BUbsmOhqurh510V5WcHE3EN7775nlH+xWf/W5T/59n1Ub6qauM/T2ULNPk5kZobwnc5dsveKD+M610tLQ9jFVba3gNZfjk/DsZu3hPlP374jLiGuTMXo3x7SRPXsOGSrPd5x5NOjPLPWnN7lK+qes/cWJS/bCF7D1VVv7D+uniN1LU7NkX57zjnsiFV8o07ZXR1lL9wLr/WXb+c3VddunBSXMN7P31elP/Fx2S90+7+bJSvqtrx+a1RPu/eqhbXZ/dmq5psj66q2v7R8B53z/64hpHxbI/k+NDbcyjKN/38WcXE53ZG+f/nmu+Na/izs98U5dcN4cfH94Uf5fQgu87MjIT3ppX3Tp+YPTuu4SWbV77veMvOB0X50zfnfWxq9chklD84yPaWqqoblrM1Zgf5ifm7V39flP+hsz8b5dcP4by8YTl7Nr+myZ+47Btk95djlT/7Wv7Ihig/uSN7blVV1RzOe8A74jeYAAAAAAAA6MSACQAAAAAAgE4MmAAAAAAAAOjEgAkAAAAAAIBODJgAAAAAAADoxIAJAAAAAACATgyYAAAAAAAA6MSACQAAAAAAgE4MmAAAAAAAAOjEgAkAAAAAAIBODJgAAAAAAADoxIAJAAAAAACATgyYAAAAAAAA6MSACQAAAAAAgE4MmAAAAAAAAOhk9JivDgb/QWXcubHLb4ryf3jN4+Ma1t/z9ijfLLVxDdsvmo3yv/eE74zyj77nO6N8VdXB/lSU/8PPPi6u4U/WHo7ys9fNxDU0vex4+O6tl0T5S2dPjPJVVZtP2B/lZ+fH4xqe/U8viPKDyXx/e+B9r4nyT101F+Wf8OXvjvJVVWuuy86J5VXHvozcFfOnLkb5l3wh/xzOOrwzW6Dfj2toB/m1guPA8nKWH8KxNLhtd5T/gy89Ia7hhx54cZT/8IUPi2tYtTP7LP/oK4+N8s/6trdG+aqqg4Osd/rjDz0pruHVG7PrxGCQ/zzbSZv3Rvn/tvUDYQWrwnzVUpsdj1+cPymu4c/en53bI0txCbXx3D1R/kfXXhXlf33no6J8VdXar2T5tolLqLVnZ+fEL33lGXENa/59R5RvB0PonZaH8GGy4pqlsHdayO4lqqr6+7L72xs+c6+4hslzsnuB2aE8vsvOqbnwvJ4Zwo/AL1Uvyr/u83kPeuEJ94zy84tjcQ1L/exzuPBBfxHle83qKF9V1W+zg/qa5em4hp/83LPjNVIPPen6KP+iDdkzxL2DvAFcCveWNUO43PcH2SIfOLw1rmH9Vdn1bmR/NhuoqmpXZfeXd8ZvMAEAAAAAANCJARMAAAAAAACdGDABAAAAAADQiQETAAAAAAAAnRgwAQAAAAAA0IkBEwAAAAAAAJ0YMAEAAAAAANCJARMAAAAAAACdGDABAAAAAADQiQETAAAAAAAAnRgwAQAAAAAA0IkBEwAAAAAAAJ0YMAEAAAAAANCJARMAAAAAAACdGDABAAAAAADQiQETAAAAAAAAnYwe68V2aSlavJmaivJVVTV2zBK/rt2f2hqX8Bs/+OYo/0cP+YG4ho3/vj/K3/qOU6L8X27fHuWrqn5u/fVR/h4P/9u4hr/ecUGU33LiNXENv731g1F+fW86yu9dc0WUr6q6bONklF83shDXsOv+q6L8ht5cXMN549nn8Jf7s/Nq/+tPivJVVevb2Si/99yJuIaffND7o/yHf/RBcQ3t/Hy2wNh4XANUVdX4WLzEyMzarISPr4lreMFDPx7l337fb49r2PrJrI+tt26K4r+wNd+bXr714ii/8Tv+Kq7hTbc/NMqvGzsc1/A7Wz4b5cearGcYhrGmF+VfuP7yuIaHfvfVUX7jSP5d3ns8uz98y6ETovx73/aQKF9VNXNoEOUPnJr/jOdvnvuuKP+y335WXMPkbf+eLdDLzomqqqZp4jU4DoTfY9vPzsmqqtETs/uy6VvzY3HDSPbs6/rlNq5hcy/7LPcNss9hsp/foz9xqh/l33jBX8Y1fGTunCi/ZiS8N62qp6+5Mspv6q2Oa0j1mux6+aCJ8F6gqt74wNdE+ZmRvIbTx7LvYqnNrrf7+4tRvqpqw0i2Nxwc5PvbqaPZffb3vDHvnc66bGeUbw/ne0OztByvcUf8BhMAAAAAAACdGDABAAAAAADQiQETAAAAAAAAnRgwAQAAAAAA0IkBEwAAAAAAAJ0YMAEAAAAAANCJARMAAAAAAACdGDABAAAAAADQiQETAAAAAAAAnRgwAQAAAAAA0IkBEwAAAAAAAJ0YMAEAAAAAANCJARMAAAAAAACdGDABAAAAAADQiQETAAAAAAAAnYwe68VmYiJavJ3K8lVVgzWTUX7bxxfjGi556slR/vYnzMc1bLp4Kcpvu2hvlP/T5nujfFXV2he8Kcr/4JrsPVRVPfGM98dr5KZX9E9f38v//Ef00hWm4hqqBmE+21uqqi6cy/a4V/7d90b5k684FOWrqpZXjUX5kSftjmu4em5zVsMNt8Y1NKtXR/l2Oj+emtnD8RqsvGZmTZQfrM336KWZbI+duXY5ruGlOx8X5c999NVxDbsvOS3KT+/KPof3vP0hUb6q6q+ele1vz193c1zDo0/6RLxGLm487vYmmux6XVV1QXypyvu3q5ey3uVXP/rcKL/lpjbKV1X1x5sov/pRt8U1vHXXg6L8hs/n91Uj69dF+XYxu7+tqmp6fl72W0HaRw/WZ71XVVV/7XiU33BFfjx/z+U/EOXffu6b4xpuWc72t63h5Xqp8j16rMmKOD+/ravzJ/M+NrdqpQtYccPone4fP9bOn4un9g+yZ9LT2bZQVVXzbXZu94ZQwzvD507bP5rfI9dI2LdsWp/XcHghX+MO6MgAAAAAAADoxIAJAAAAAACATgyYAAAAAAAA6MSACQAAAAAAgE4MmAAAAAAAAOjEgAkAAAAAAIBODJgAAAAAAADoxIAJAAAAAACATgyYAAAAAAAA6MSACQAAAAAAgE4MmAAAAAAAAOjEgAkAAAAAAIBODJgAAAAAAADoxIAJAAAAAACATgyYAAAAAAAA6MSACQAAAAAAgE5Gj/Xi0qmbo8UX149H+aqqtmmifDNo4xre+daHR/mXPecNcQ2/8bQfjvKn/f2tUX77B/dE+aqql7Y/FOWv+IkPxTW8ZPNl8Rp8a/jVnfeP17jwddnecOr7svOqnTzmFn6X3Pi4bJ9+w33+Lq7hea/4+Sh/4uR1cQ3tzOpsgaXluAa+NSyeujHK7ztzMq6hH7ZfY7NxCfXu9z8gyv/7D/9RXMP5P/KjUX7tG9ZG+U2X5vvCq/72e6L8B55yRVzDm05/b5TvNX6e7XjRbwdR/uduyfqeqqoLP37/KL/1k9mf347k94a7nrwQ5S+/35vjGu79up+N8mfvvzGuIe2dmrn5uIaaz74Ljg/L66ai/IHTs3xV1fJU9txpdD7fW/Z+4sQov3SPbI+vqloT7pEHw49hzRB6hluXD0X56ZFeXMPMSH5M8q1h/+DwENboR/mx8M8fC5/LV1WlZ9WW3qq4hke/K3smfc8vZ8/Vq6oWwmcFo/vzvqd3+JvTO7njAwAAAAAAoBMDJgAAAAAAADoxYAIAAAAAAKATAyYAAAAAAAA6MWACAAAAAACgEwMmAAAAAAAAOjFgAgAAAAAAoBMDJgAAAAAAADoxYAIAAAAAAKATAyYAAAAAAAA6MWACAAAAAACgEwMmAAAAAAAAOjFgAgAAAAAAoBMDJgAAAAAAADoxYAIAAAAAAKCT0WO9uO/s6WjxkeU2yldVjc8OonyzGJdQ2z4xH+X/7annxTX8w3P/IMr/p7lfivIn/ettUb6qausnDkb5C3dfENfwuodmazzjgk/GNfyXTR+P8v3Kz6vUfJvV8LPXPi2u4UufPj3Kb/l0XEJt+8r+KN9OHnML/rpufMLaKF9V9ezv+kCU/8lX/nxcw/bXfCHKL93/7LiGue2TUX7mo9fFNbRLS/EarLzZbRNRfml1M6RKvnH9fn6dmbk6y3/PFfl14t8e8FdR/pF7s/3txH/N9viqqg2X96P8dbeeE9dw1rdn19vH3/fLcQ2/se3dUX5Nk/1M3TB6r4ODbI3f3fGkuIb3XXKvKL/xk/kxvX1/dm+XfhW7HpD/fOWLH/iuKH/Ou34qruHcv98b5Qcb1sQ1LK2fivLjl2bv4UgRQ7jZZ8XNnpj14fMb8/N6aVWWH53P+7eZq7MN7sHve2Fcw8cf90dRflV4vd0zCK8RVXVCL7tW7eovxzVcv5w9Q9w8ktewpZc9z/1WcPvgcLzGLcvZ8TQzkvXyVVWbw2P64CA7npbC539VVdtGV0f5H7vhkXENJ78321/6m/Lnbwvrx6L8xA174hrag7PxGnfEbzABAAAAAADQiQETAAAAAAAAnRgwAQAAAAAA0IkBEwAAAAAAAJ0YMAEAAAAAANCJARMAAAAAAACdGDABAAAAAADQiQETAAAAAAAAnRgwAQAAAAAA0IkBEwAAAAAAAJ0YMAEAAAAAANCJARMAAAAAAACdGDABAAAAAADQiQETAAAAAAAAnRgwAQAAAAAA0EnTtu2dvvjUj/zsnb94Fyy3Kz+/muwtxWuMj/Sj/A9u+VRcw9lju6P8jcszUf4F//jcKF9Vdfbf7Y/yg8nRuIZmeRDlD5y5Oq5hbkt2XvQWotNyKEaWs/zMtQtxDWO75qL8YHosrqEdzb7L6546HeV//qnvjPJVVa/+2++O8ie/4rNxDSMTE9kCWzfHNbST2fEwctvevIZ+tj9duPPVTVwEsbR32jgxG9cw0mTXiZEmOxarqsbCNXYvropr2Dp5IMr/yIaPR/lfvOoHonxV1cG3bYvyU3vy7zK1sCa/H5jfFG5vx8HuOBLekqy6dQjn5Wy2xmAs/yDn12XHw+5vzz7I5z/4oihfVfVXFz4+yt/jT26Ka2j3Z/tbM7M2rqFGsu+y3bsvr6HJarjw9r88DnYHnv7x50eNy8lTeR8+FT43WhjkzypW97J75M/vOzmu4QmbL4vyP7Puxih/aDAf5auqrl/O+uCT8q8yttTm1/y0BZxve1G+V/lzq/R+Yk14T1RVNT2SfQ5jleWrqq5fzh7AbQ1LWN/LnltVVT31qu+I8rO/eWJcw+TVt0X5/ubsuXpVVTOffZcju/PrXU1mz9/edc3L77B3WvkJEAAAAAAAAHcrBkwAAAAAAAB0YsAEAAAAAABAJwZMAAAAAAAAdGLABAAAAAAAQCcGTAAAAAAAAHRiwAQAAAAAAEAnBkwAAAAAAAB0YsAEAAAAAABAJwZMAAAAAAAAdGLABAAAAAAAQCcGTAAAAAAAAHRiwAQAAAAAAEAnBkwAAAAAAAB0YsAEAAAAAABAJwZMAAAAAAAAdNK0bXunL/75FY+68xfvgl4NknhVVc0NJqL85tEDcQ39cA53/cKmuIbX/ctjo3xz9qEo/4mH/UWUr6r6yeueGuWvfvM5cQ0z1y5F+bEDy3ENIwv5GiutWcrO7eWZ7LyuqlpYPxbl95zbi2t4zrPeHa+R+Iffe0K8xrq3fT7K9zZtjGuoponi7VJ2XldVNWPZ8VSj+fHUzh6O8hfufHX2QTIUn7v+lKh32jeYimsYabI9ehj921jTj/L/euD+cQ1vfNcFUX4wEX2V9amnvzzKV1W9Zt/9s/w7Hx/XsPr6LD82G5dQvcXsu0jzzTHul+6ycInlqfznAufXZZeJwyfkl5kff0bWO33X6i9G+Wf82S9F+aqqU/7sS9kCvbxnqNHRKN5MjOc1hP3bMLSHw97ptj9f+TdBXXrDidEOudDm59TMSHY/sVT5obTUrvzPf//iNU+P8tfuyu4N3/+wV0f5qqrNvexZw1eWhvDMJ7zobx3CZWJ6JLy/DS212b3AkTWye5J9g/ye5mCbXW/nBvn3cP5kdkDctJw9D37sx18Q5auqTntVtr+NXb8rriHVP2FdvMbIgaxvGYqR7Lu48Mu/d4cXvJW/ggEAAAAAAHC3YsAEAAAAAABAJwZMAAAAAAAAdGLABAAAAAAAQCcGTAAAAAAAAHRiwAQAAAAAAEAnBkwAAAAAAAB0YsAEAAAAAABAJwZMAAAAAAAAdGLABAAAAAAAQCcGTAAAAAAAAHRiwAQAAAAAAEAnBkwAAAAAAAB0YsAEAAAAAABAJwZMAAAAAAAAdNK0bXunL57ztt+68xfvyuJNkj5icX40yg/CfFXV1PVjUf7ED83FNfQOL0X5nQ+bifL7HrAY5auq/uiRb4zyp43tiWv42z0Pi/Jv+9SD4xqmb8iOyTYcCw8motO6qqrGDmYn99rH7Yhr+O9n/1OU3zeYjmv4pU8/Pcqf+ceDrICLL83yVdWbWRvlm8mJuIYaz/bYGuTHdDu/kC0wszquofYdiOIX3vbnQ7jqknrYe341PyBDu/auyRa4Lt8f2/BoXH95XEKtvT47r+dOGI/ytz5hOcpXVb3hMX8Z5e8xdjiu4aLD26L8K655fFzDLTdsjPLNQtg8rc368Kqqdjmr4aHnXhPX8PytH4ryO5az+4mqqhd/8vui/Clv6UX5iXd9LspXVTVjWS8/MjGE3imsoZmcjEto57L9pZkJr1VV1c5mNVy489V6p+PAUz/ys1HvtHFiNq5h/1J2Tvz7jSfFNSwfyPqO8d3Z/lhVNbkrOyXasITZB+R9y4se8L4o/zPrboxr6LfZff6BwXxcw+cWsz12dpBdq04Z3Rvlq6r6lR2PD5zIzqlhuHX5ULzGU77wE1F+7K0bovym910b5auq2sNhzzCEviUdUiydsTUuYXTn/myBsP+rqqql7B71witfdocfpN9gAgAAAAAAoBMDJgAAAAAAADoxYAIAAAAAAKATAyYAAAAAAAA6MWACAAAAAACgEwMmAAAAAAAAOjFgAgAAAAAAoBMDJgAAAAAAADoxYAIAAAAAAKATAyYAAAAAAAA6MWACAAAAAACgEwMmAAAAAAAAOjFgAgAAAAAAoBMDJgAAAAAAADoxYAIAAAAAAKATAyYAAAAAAAA6adq2vdMX7/Fbf3jnL94FvfkkfcTs6UtRfs0VY3ENWz81F+XbkSauYXnVaJRfWNeL8qOHB1G+qmrw07uj/MO2XBvX8I/vPz/Kf+djPhPXcOLEvih/4/yGuIbU7sVVUf5Tl58R17Dm0vEof8JnD8c1jF1yXZRvD2c1NKumo3xVVY1ke0PTG8LPKTThHjmS19AuLET5ZnV2TlRV1cJiFH/Xza/KLzbETv+jl0e9U9PPaxg7dTbKD65cHdew4UvRx1DLU/nhvLQ6W2M53GLH92efQVXVlmfcEOX/9Mw3xzU89TM/FeXvu/XWuIb7rLklXiMxP8jvJ66b2xjlP3/riXENC9etifInv2c5rmHyoi9F+cF8doPZjGX9Y1VVMzmR5Uez+7qqqgrvL5ux/JiuXtZDtmvz3qlZyJ4VvOsrv693Og6c9qd/EF0w2/H8WcVD73N1lL/l0Excw973bYvyad9SVbWwOWtE2+ksP3FjvkcvbM32hXc+6Y/jGn7kkudE+fO3XR/XcP6ar0T5uUF2rTs4mIzyVVXXH94U5S+66cy4hkN7shNrw8X59faEj+6J8u0NYR89yPfYuHdam/WwRxbJLvnDeLafPrta3px/DiPz2R75nk//tzv8IPwGEwAAAAAAAJ0YMAEAAAAAANCJARMAAAAAAACdGDABAAAAAADQiQETAAAAAAAAnRgwAQAAAAAA0IkBEwAAAAAAAJ0YMAEAAAAAANCJARMAAAAAAACdGDABAAAAAADQiQETAAAAAAAAnRgwAQAAAAAA0IkBEwAAAAAAAJ0YMAEAAAAAANCJARMAAAAAAACdjB7rxf65h6LFt2/aF+Wrql5wyoei/Jvu9ZC4hhsetz7K9wdNXMPi8jG/qq9rpGmj/Ikz+6N8VdVLT397lF9q83no1Q/dFOU/veuUuIZ/u+yBUf6sNxyM8jsfPhPlq6oOPvxwlD/7NUtxDb0vXpEtsJTXUJMTUbyZmsr+/H4/yw+jhiFow++i6fXyGk7cktWwfzauIT2eOD5MnJz1Tiev3xfX8Ioz3xrl33LOg+Iabgp7p6levkcvDLLeaWwk22OvPpD1HFVVLzj5g1F+ZiTvQR91ytVR/qIbzoxruPjzZ0f5rR/JPofZrXkPOv/wbG/Y8vf59XrN+y+L8v1D+bWuDa/Zzdh4VsAQzomRtWvCBfLjqT2c9eI1mu2PVVVLJ2d73Mj8clzDyBA+S1beCWftjvKnr90T1/DKU94Z5Xf08/uRz52aPWs4Y/y2uIaNI9neMlLZc6eL5rLrfVXV5tHsecmGsP+rqvqhMz4T5W9bXBvX8J4994nyF3/onlG+N59fb+/7xOyZz8iH18U13PNfd0b59uYdcQ01hOcdkSFca5s1q6N8Oz2Z1zA3n+WX8r3hwAO3R/nxfXnv1JtdjNe4IzoyAAAAAAAAOjFgAgAAAAAAoBMDJgAAAAAAADoxYAIAAAAAAKATAyYAAAAAAAA6MWACAAAAAACgEwMmAAAAAAAAOjFgAgAAAAAAoBMDJgAAAAAAADoxYAIAAAAAAKATAyYAAAAAAAA6MWACAAAAAACgEwMmAAAAAAAAOjFgAgAAAAAAoBMDJgAAAAAAADoxYAIAAAAAAKCT0WO9eOUFr/+PquOb5mmr3xevsdT2VzRfVbVU+RqJXjXxGtcvt1H+i4vb4hpGmqyGPZ/bEtcwupx9lsvrJrICso+gqqre/PC/iPI/fMUvxDWc9u+DKN9MTcU1VG+FZ/Rtfl7W8nKWnxjPa1jIDsp2KjwnqmowNRblm/n8c2jHjnlJ5m7isoe/YaVLqKpsf/vNzZfGFfTbbI9eHkLf02+HcMFL5G1LzbVLUf4zCxviGvrhtWZuf369XX/Kvii/tCr7HNZ/Jfseqqpe8DPviPK/fvUz4xpW/dtClG96vbiGJrzWNaPhtXIk753a+fko36xZHddQTdiDjmd9z/FieWZypUtgCD5xv39Y6RKqalWU3pJvj3Xe+G35IrEh3CMH7jl+84r++Ufke/T2sX1R/rVXnB/X8IJ7fjjKf3b+XlF+y+fC5wxV9VfPe2eUf+xjN8Y1NG9fzBYYxj1+2H81/fC+aiJ/3lJL2fEwmMw/x5GFle+dBuG1YuxgeDxW1fLMEL7PO+A3mAAAAAAAAOjEgAkAAAAAAIBODJgAAAAAAADoxIAJAAAAAACATgyYAAAAAAAA6MSACQAAAAAAgE4MmAAAAAAAAOjEgAkAAAAAAIBODJgAAAAAAADoxIAJAAAAAACATgyYAAAAAAAA6MSACQAAAAAAgE4MmAAAAAAAAOjEgAkAAAAAAIBODJgAAAAAAADopGnbdqVrAAAAAAAA4G7EbzABAAAAAADQiQETAAAAAAAAnRgwAQAAAAAA0IkBEwAAAAAAAJ0YMAEAAAAAANCJARMAAAAAAACd/L/3jb8A2iHdswAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 2160x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 2160x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "from matplotlib import pyplot\n",
    "import spm1d\n",
    "\n",
    "fig, ax1 = plt.subplots(1,3, figsize = (30,12))\n",
    "\n",
    "for ii, c_ax in enumerate(ax1.flatten()):\n",
    "    c_ax.imshow(X_test[1,:,:,ii], interpolation = 'none')\n",
    "    c_ax.set_title('RGB: {} -- Test {}'.format(ii+1, y_test[1]))\n",
    "    \n",
    "X_train1, y_train1 = X_train[np.where(y_train[2,:] == 1)], y_train[np.where(y_train[2,:] == 1)]\n",
    "X_test1, y_test1 = X_test[np.where(y_test == 1)], y_test[np.where(y_test == 1)]\n",
    "X_test1_pred, y_test1_pred = X_test[np.where(y_pred == 40)], y_test[np.where(y_pred == 40)]\n",
    "\n",
    "# print(X_test1.shape)\n",
    "\n",
    "fig2, ax2 = pyplot.subplots(1,3, figsize = (30,10))\n",
    "\n",
    "for ii, c_ax in enumerate(ax2.flatten()):\n",
    "    xxx, yyy= X_test1[:,:,:,ii], X_test1_pred[:,:,:,ii]\n",
    "    xxx_2   = np.array([yy.flatten() for yy in xxx])\n",
    "    yyy_2   = np.array([yy.flatten() for yy in yyy]) \n",
    "    snpm    = spm1d.stats.nonparam.ttest2(xxx_2[:30], yyy_2[:30])\n",
    "    snpmi   = snpm.inference(0.01, two_tailed=True, iterations=1000)\n",
    "    \n",
    "    print(xxx_2.shape[1])\n",
    "    J,Q     = xxx_2.shape\n",
    "    z       = snpmi.z\n",
    "    zstar   = snpmi.zstar\n",
    "    z0      = np.zeros(Q)\n",
    "    z0      = z\n",
    "    Z0      = np.reshape(z0, (30,30))\n",
    "    Z0i     = Z0.copy()\n",
    "    Z0i[np.abs(Z0i)<zstar] = 0\n",
    "    ZZ      = np.hstack( [Z0, Z0i] )\n",
    "    \n",
    "    c_ax.imshow(Z0i, 'jet')\n",
    "    c_ax.set_title('RGB: {} of Set {} vs. Set {}'.format(ii, 1,2))\n",
    "    c_ax.axis('off')\n",
    "\n",
    "fig3, ax3 = pyplot.subplots(1,3, figsize = (30,10))    \n",
    "\n",
    "for ii, c_ax in enumerate(ax3.flatten()):\n",
    "    c_ax.imshow(X_test1[1,:,:,ii], interpolation = 'none')\n",
    "    c_ax.set_title('RGB: {} -- Test {}'.format(ii+1, y_test1[1]))\n",
    "    c_ax.axis('off')\n",
    "    \n",
    "fig4, ax4 = pyplot.subplots(1,3, figsize = (30,10))     \n",
    "\n",
    "for ii, c_ax in enumerate(ax4.flatten()):\n",
    "    c_ax.imshow(X_test1_pred[1,:,:,ii], interpolation = 'none')\n",
    "    c_ax.set_title('RGB: {} -- Test {}'.format(ii+1, y_test1_pred[1]))\n",
    "    c_ax.axis('off')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0. 0. 0. ... 0. 0. 0.]\n",
      "(1769, 30, 30, 3)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 2160x864 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(y_train[:,2])\n",
    "\n",
    "\n",
    "X_train2 = X_train[np.where(y_train[:,2] == 1)]\n",
    "print(X_train2.shape)\n",
    "\n",
    "fig, ax5 = plt.subplots(1,3, figsize = (30,12))\n",
    "\n",
    "for ii, c_ax in enumerate(ax5.flatten()):\n",
    "    c_ax.imshow(X_train2[1,:,:,ii], interpolation = 'none')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Kolmogorov-Smirnov Distance Measure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8765151515151515\n",
      "0.8827137546468401\n",
      "0.8908461538461538\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def Kolmogorov_Smirnov_Dist(XX, YY):\n",
    "  \n",
    "    import numpy as np\n",
    "    nx = len(XX)\n",
    "    ny = len(YY)\n",
    "    n = nx + ny\n",
    "\n",
    "    XY = np.concatenate([XX,YY])\n",
    "    X2 = np.concatenate([np.repeat(1/nx, nx), np.repeat(0, ny)])\n",
    "    Y2 = np.concatenate([np.repeat(0, nx), np.repeat(1/ny, ny)])\n",
    "\n",
    "    S_Ind = np.argsort(XY)\n",
    "    XY_Sorted = XY[S_Ind]\n",
    "    X2_Sorted = X2[S_Ind]\n",
    "    Y2_Sorted = Y2[S_Ind]\n",
    "\n",
    "    Res = 0;\n",
    "    E_CDF = 0;\n",
    "    F_CDF = 0;\n",
    "    power = 1;\n",
    "    height = 0;\n",
    "\n",
    "    for ii in range(0, n-2):\n",
    "        E_CDF = E_CDF + X2_Sorted[ii]\n",
    "        F_CDF = F_CDF + Y2_Sorted[ii]\n",
    "        if XY_Sorted[ii+1] != XY_Sorted[ii]: height = abs(F_CDF-E_CDF)\n",
    "        if height > Res: \n",
    "            Res = height\n",
    "\n",
    "   # KS_Dist = Res**power\n",
    "    \n",
    "    return Res**power\n",
    "\n",
    "def  Kolmogorov_Smirnov_Dist_PVal(XX, YY):\n",
    "    import random\n",
    "    nboots = 1000\n",
    "    KSD = Kolmogorov_Smirnov_Dist(XX,YY)\n",
    "    na = len(XX)\n",
    "    nb = len(YY)\n",
    "    n = na + nb\n",
    "    comb = np.concatenate([XX,YY])\n",
    "    reps = 0\n",
    "    bigger = 0\n",
    "    for ii in range(1, nboots):\n",
    "        e = random.sample(range(n), na)\n",
    "        f = random.sample(range(n), nb)\n",
    "        boost_KSD = Kolmogorov_Smirnov_Dist(comb[e],comb[f]);\n",
    "        if (boost_KSD > KSD):\n",
    "            bigger = 1 + bigger\n",
    "            \n",
    "    pVal = bigger/nboots;\n",
    "\n",
    "    return pVal, KSD\n",
    "\n",
    "C_num = 3\n",
    "\n",
    "# Separating Wrong Responses of the CNN Classifier\n",
    "X_test_wrong, y_test_wrong = X_test[np.where(y_test != y_pred)], y_test[np.where(y_test != y_pred)]\n",
    "\n",
    "# print(X_test_wrong.shape)\n",
    "\n",
    "# Finding Wrong Decisions for Label 2 (just an example)\n",
    "X_test_wrong3, y_test_wrong3 = X_test_wrong[np.where(y_test_wrong == C_num)], y_test_wrong[np.where(y_test_wrong == C_num)]\n",
    "\n",
    "# print(X_test_wrong1.shape)\n",
    "\n",
    "X_train3 = X_train[np.where(y_train[:,C_num] == 1)]\n",
    "\n",
    "\n",
    "fig4, ax44 = pyplot.subplots(1,3, figsize = (20,6))\n",
    "\n",
    "KSD_T = []\n",
    "\n",
    "for ii, c_ax in enumerate(ax44.flatten()):\n",
    "    # Comparing X_train for Label == 2 with X_Test_wronge for Label == 2\n",
    "    xxx, yyy= X_train3[:,:,:,ii], X_test_wrong3[:,:,:,ii]\n",
    "    xxx_2   = np.array([yy.flatten() for yy in xxx])\n",
    "    yyy_2   = np.array([yy.flatten() for yy in yyy]) \n",
    "    \n",
    "    KSD = np.zeros(900)\n",
    "    pVal = np.zeros(900)\n",
    "    \n",
    "    for kk in range(1, 900):\n",
    "        KSD[kk], pVal[kk] = Kolmogorov_Smirnov_Dist_PVal(xxx_2[:30,kk], yyy_2[:30,kk])\n",
    "    \n",
    "    KSD2 = KSD\n",
    "\n",
    "    print(1 - KSD2[KSD != 0].mean())\n",
    "    \n",
    "   # KSD_T[ii] = 1 - KSD2[KSD != 0].mean()\n",
    "    \n",
    "    KSD2[pVal > 0.05] = 0\n",
    "    \n",
    "    J,Q     = xxx_2.shape\n",
    "    z       = KSD\n",
    "    zstar   = KSD.mean()\n",
    "    z0      = np.zeros(Q)\n",
    "    z0      = z\n",
    "    Z0      = np.reshape(z0, (30,30))\n",
    "    Z0i     = Z0.copy()\n",
    "    Z0i[np.abs(Z0i)<zstar] = 0\n",
    "    ZZ      = np.hstack( [Z0, Z0i] )\n",
    "    \n",
    "    c_ax.imshow(Z0i, 'jet') # Can be replaced with Z0i\n",
    "    c_ax.set_title('RGB: {} of Set {} vs. Set {}'.format(ii, 1,2))\n",
    "    c_ax.axis('off')\n",
    "\n",
    "#print(KSD_T.mean())    \n",
    "\n",
    "fig3, ax7 = pyplot.subplots(1,3, figsize = (20,6))    \n",
    "\n",
    "for ii, c_ax in enumerate(ax7.flatten()):\n",
    "    c_ax.imshow(X_train3[1,:,:,ii], interpolation = 'none')\n",
    "    c_ax.set_title('RGB: {} -- Test {}'.format(ii+1, 3))\n",
    "    c_ax.axis(\"off\")\n",
    "    \n",
    "fig4, ax8 = pyplot.subplots(1,3, figsize = (20,6))     \n",
    "\n",
    "for ii, c_ax in enumerate(ax8.flatten()):\n",
    "    c_ax.imshow(X_test_wrong3[1,:,:,ii], interpolation = 'none')\n",
    "    c_ax.set_title('RGB: {} -- Test {}'.format(ii+1, y_test_wrong3[1]))\n",
    "    c_ax.axis('off')   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Wasserstein Distance Measure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8989742152466368\n",
      "0.8937466517857143\n",
      "0.8949921962095875\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABGgAAAFkCAYAAAB8cA3sAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAA3NUlEQVR4nO3de6xu6V0f9me9t309e+9zm8uZGc947DG+YeMArYEGnDQISEAhqEkjtWqqqKiqlKpVpaAg0RA1TZuipqXqLa1UBZK0DSEVKaIFEkQBk2BjiGXjMTbGc/H4zMy5zd77nH19b6t/nG3PDJnZfJ8z55014/l8JCTm7K+f91lrPbf12++cadq2LQAAAAB0p9d1BwAAAADe6hRoAAAAADqmQAMAAADQMQUaAAAAgI4p0AAAAAB0TIEGAAAAoGMKNAAAAAAdU6B5C2qa5qmmaQ6bptlrmub5pml+omma9T+Q+aamaX6uaZrtpml2mqb5bNM0f6NpmrMnP/93m6aZnbSx1zTNE03T/AcVfRg1TfOPTvrSNk3zkbt4fX/0Jf3aP2l/7yX/97Y7aLNtmuadp/z8jzVN8zsn9+pG0zQ/0zTNA6/tSgC68QbZJz7cNM0/bZrmhaZprjVN89NN09x/l67PPgFwh94ge8R7m6b5rZP2t5um+aWmad57l67PHkFnFGjeur6vbdv1Uso3lFI+VEr54a/8oGmaby2l/Eop5Z+VUt7dtu1WKeW7SynTUsoHX9LGb7Rtu37Szr9RSvmxpmk+VNGHXy+l/NullOfv/DL+ZW3bfvQl/XrfyR9vfeXP2rb90t38vBOfLaV818m9ulRK+UIp5X9ewOcAvF663ifOllL+11LKI6WUh0spt0opf+fOL+dF9gmA16zrPeLZk//NuVLKhVLKz5ZS/sFruJ6vskfQJQWat7i2bZ8vpfxiub24fsWPlVL+Ttu2/2XbtldOcl9q2/ZH27b9lVdp51+UUn63lPKe8HPHbdv+eNu2v15Kmb2GS6jSNM1m0zT/W9M0zzVNc7lpmv+8aZr+yc/e2TTNrzZNs9s0zfWmaX7q5M9/7eR//qmTqvm/+QrXc6Vt22df8kezUsqrVskB3iw63Cd+vm3bn27b9mbbtgellP+hlPJtd34lGfsEQK7DPWKnbdun2rZtSylNeZ3WVHsEizbougN0q2maB0sp31NK+eWTf14rpXxLKeVHKtv55lLKu0opv/WSP/t0KeVvtm37f9y1Dr92P1lKuVJuL3hrpZSfK6U8U0r5X0opf72U8k9KKX+slDIqpXxTKaW0bfvtTdO0pZQPtm37+6/W8MnXHT9dStkotxfVH1zcZQC8Pt5A+8S3l1Ier/nMO2SfAAh1vUc0TbNTSlkvt7948Fcru38n7BEslG/QvHX946ZpbpXbC8rVUsqPnvz52XJ7XHz1XztqmubHTv59yP2maV662H745M/3Sim/WUr5e+X21/FKKaW0bfuBN1Jxpmmae8vtDeQ/btt2v23bq6WU/7aU8udPIpNy+2v0l9q2PTr5dk/s5DcDW+X21yx/pJTyubvWeYDX3xtmn2ia5gPl9sH7L7/Wi/pDPsc+AZB5Q+wRJ2vqZinlL5VSPvnaL+vV2SN4PSjQvHV9f9u2Z0opHymlvLvcXghKKWW7lDIvpXz1L2Js2/aHThaLnykv/9bVx9q23Tr59zPvK7f/Hc3/4m539A/8RV2Pn/zZ4y/5sz8aNvVwKWVYSnnuZDPYKber3fec/PyHyu2vSP7mSft/8U7627btC+V2df3/bprGt9SAN6s3xD5x8pcq/nwp5T9q2/ajr5KxTwC8vt4Qe8RJ+/ullL9dSvm7TdPc8wd/bo/gzUSB5i2ubdtfLaX8RCnlvz755/1SysdLKT9Q2c6VUsr/VUr5vrvcxZf9RV1t277v5M/e95I/e8UD+yt4ppRyXEq5cLIZbLVtu/GSNp9v2/YH27a9VEr590sp/1Nzyt+2/ocYlNuL9cYd/u8B3hC63Ceapnm4lPJLpZS/3rbt3zulbfsEQAfeQO8SvVLKainlX/ovH9kjeDNRoKGUUn68lPKdTdN8w8k//1Ap5S82TfNXvlKFPvn3S9/+ag00TXO+lPJnSsXfD9A0zVLTNMsn/zhqmma5aZrmDvofadv2uXL73wv9W03TbDRN02ua5h1N03zHSX/+7Ml1lnK7+t+WF/8C4yullEdfre2maX6gaZqvO2nzYinlvymlfPKkAg7wZvfj5XXeJ5rb/3nRXy6l/I9t2/7tO+96zj4BcEd+vLz+e8R3Nk3zoaZp+k3TbJTba+p2uf0XDS+EPYLXgwINpW3ba6WUv1tK+U9P/vnXSyl/vNz+Cxl/7+Tre79Qbv/n8v77l/xPv+UrXw0stxfDa6WU//ArPzz5at+/dcpHf76UclhuV7p/8eT/f/juXNWr+nfK7b+067Pl9sL5j8qLX8H85lLKx0+u52fL7a/TP3nys79WSvnJk68z/rlXaPeBcvse3Sql/E65/dXOP7OoiwB4PXW0T/x75fZh9kdf8jX0vbt6Ya/MPgFQoaM9YquU8n+WUnZLKV8st//S3u9u2/borl3YK7NHsFBN27Zd9wEAAADgLc03aAAAAAA6pkADAAAA0DEFGgAAAICOKdAAAAAAdEyBBgAAAKBjg9N++O3f92Pxf+LpeKMff+jwcJ5nb87+8NCJ+bCJcrOV7utSS9fHcXY+yu/t0flTH+nL7N+ftztdiaNleCvPLm/nYyHVm+b/ZbJRxfga7E/jbM0z643zPvSm+f0ab47ibDvI5k4ppSzdOI5y09Vh3GbNnGwqnm9vspj/St3/90t/Jb9hX+M+8t3/VXyTxxX7xOAgH+uDw3wOTVeyPqT7ySKtPHcYZ2dr+XybD/P5VvPM5hXryCLW6ZrPr3m+g5ozy062PtZqJnkfegd5H+arS3G2Zoz19ydRrq0Yi4vaV2vUPId/8tt/rftF5A3ij33n38zfJ7byc+zSTn4u6x/m2dlK1ofpaj4maxxv5vNi+UY+1ocHFefYQd6HmvtQc4ar6W+65tSYbObrY835vGo9X9Ba1tvZz8PDfE7O1/J71kyya2uHi5ln6ecv0i9+8j97xX2i+0oFAAAAwFucAg0AAABAxxRoAAAAADqmQAMAAADQMQUaAAAAgI4p0AAAAAB0TIEGAAAAoGMKNAAAAAAdU6ABAAAA6NjgtB820zZuaOnmLM7Oh81Csu0gy/YP53Gbo91xnO0d5Nl22I+z07VTH9PL9Cf5M2umcbQMb+XZ0a28D6l5+GxrDfbzmzC4eRRn56ujO+lOZ2rmejrOe4O8/jtbybO9ijHeG+frEnemN6lYT28usCOhdE8Z3axYG3aO42wzWcyYnK0NF9Ju1XxbUDZVc16o0T/Ix0LvYLKQPsxXF/N8F6V3kM+J2OZKHG0q1qWaOVlzduNFvWn+PAYHeXY2qjg7TPPsrYeyM9zoVt7X9af34+za7y9g/pRSJvesx9maZ1ZK9/NiPsr6UHMurBlfNdnla4dxtrefj4V2lL8vlmGerWk3fQ6llDLYyeZE1c5ecV2LUvUcXoVv0AAAAAB0TIEGAAAAoGMKNAAAAAAdU6ABAAAA6JgCDQAAAEDHFGgAAAAAOqZAAwAAANAxBRoAAACAjinQAAAAAHRMgQYAAACgY4NTf7g/jRuaj/pxdvn5ozjb396Ps+3w1Mv5qsnF9bjNw/uW42z/cBRna+5tTXa2UtGHozbOTtabODsfxtHSCy+tN837unRzlnegQjvMx3gz6b4Po91xnF1UfxdhcDCJs/OBGvSi9ffz59FU7BO9cT4m+7uHcXawsxRnU+OLK3e9zVJK6R/ka/+insMfckx4mfFGTbu5dP3vTfJ9YnCYj6+asVizRtfoVax7zSQfN72DvA+L2Cea/Xzuls3FzDMWr2Z9Gm/kh8jBQT4mh5d34uy5/bU4m5psVuw9FdnedB5na55DjaVJ3ofZSr6n1EjX6Zp1rOYcvSjtKL9fzX7+fl2jZk8Z1PQhfG9vtm/GTc7vPRdnm3F+XaXiHuRvzK/O2wsAAABAxxRoAAAAADqmQAMAAADQMQUaAAAAgI4p0AAAAAB0TIEGAAAAoGMKNAAAAAAdU6ABAAAA6JgCDQAAAEDHFGgAAAAAOjY47YfHF0ZxQ7NhE2eHu3G0zNeX42xvey9Mrsdt9g/ncXa80Y+zs5XF1MZ6k7Yim7c7H1ZkTx1VfzCbjZveNL+upiJbox3mz7eZzBbSh0WpubbZSsVgCC3dOL7rbZZSSm+az9832zN7o5iP8rHTDvN1r38tXc9LmW2uxNneQbbw1cyJ4U4+fidbS3G25n7NKrKDiv7W9GE+yBf/dO0vJd/XBof5HG4m+dpQo2YdaSbThfShHVZswhVq+hv3Ye3uz93bn5/P3/lqvqctatzwoslqvuZUnR0q5kX/Wvai0q7m7yg1J6fJZr5P1Khpd3T9IM7WvNHU7CmL0IzzdSxfRRbXh+bgKM5Wrf012Yq1v2afmK+F8+fsRv75+/n9qroHNe7C3u4bNAAAAAAdU6ABAAAA6JgCDQAAAEDHFGgAAAAAOqZAAwAAANAxBRoAAACAjinQAAAAAHRMgQYAAACgYwo0AAAAAB1ToAEAAADo2OC0Hw5vzuKGhhUfOlvJ0/PN5Tg7CnO96Txuc7A/jbM1js6deuvv2NIkf2Y1epOKbMUtGxzlzyLVDpo4WzMWmop72w77C2m3dzDuvA9N2G7NPJ+u5tneOO9r/zAfuL294zjLi2qeR2/nKM7ONlfy7Fo+fprJ3V9zegf5OKvZK48v5PtfjWZS04vF6E3bu56dD/PfOdX8dqrZz59vM8k3wJpsjXaYny8W1Ye03flqPsbnFfvEfJTvf4OKdal3kGd5Uc3z2Pzc7kL6MLlnPc4Or979z+/t52eMUcWZbHxhNc5OV/Pn0F9L36rqzgFda0f5+jiruAc1BvsV68hxfu7P335KyXfgUpq9g4p0rrezF+Xain2iXcuzNc+3v1/xHGqe76vwDRoAAACAjinQAAAAAHRMgQYAAACgYwo0AAAAAB1ToAEAAADomAINAAAAQMcUaAAAAAA6pkADAAAA0DEFGgAAAICODU774dLl3bihdunUpl7m6P71ODtbyWtIR+c3o1x/0sZt9iqySzeOF9LufNgspN3eNM+Wkvdhng+FMh9k7db1tebz8/FVU82sabfUZFeGcbQ3neftVmiH/Sg3H2W5N4qaNYwX9XcP42w7zO9xzfiZDyv2iftX42yqZn0aXcvv19L1ozg7W8vXhrbifi1KuvaXUsq04hyQqpntNfe2mcwqstM4WzV3VpfibCl5tuba0n2iZizWrAm98WKeQxlP8ixfNdg+yMMVz2O+tXYHvfnD3XzvuSjXH1es/TfzsTO8upe3ez2/t/21UZyt8UY476V9aCvW8xr9/e7Xhpp9ol1bzhuuaLfZvhln51tZPWBWMW5r1v6qfWKcr0tVe8qr6P6UBgAAAPAWp0ADAAAA0DEFGgAAAICOKdAAAAAAdEyBBgAAAKBjCjQAAAAAHVOgAQAAAOiYAg0AAABAxxRoAAAAADqmQAMAAADQscFpP2yXTv3xy4wvrMXZ2cpi6kLDg3mU6x9muVq9g3Gcze9sKdPVYZw9Op+3fHgxfw6T9ThammkTZ6crWa43yds8Optf1/zh/N4Ojto4u355EmdHu/m4mQ/yaxtvjuJsO8jvbzPN70OqN8nb7E3z+dtMZnfSHSo0k2mcnW2GE76UMlutWSVzvXD89g/y66pRMyZ7B8cV7ebzYraWr3uTjTw7X8wjK/Nhtj4NwjNAKaVMK84h442lODuouF9L1/Nsf/cwztaMsXbYj7OTi6txdhFqxnjvIN+Da9awMsqfGS9qDo7i7HwrP3DOR/n4rRk/SzvZmKg5j/T38zFZKsZkr+Le9nbyLowfuZBnK9a9yepi3gFHN7N1r+a8W9fXfJ8Y3czPQsvXFrP2N+PFnHFmD16Ms+n8bYcV73VxspT+fv7+VTMn7wbfoAEAAADomAINAAAAQMcUaAAAAAA6pkADAAAA0DEFGgAAAICOKdAAAAAAdEyBBgAAAKBjCjQAAAAAHVOgAQAAAOiYAg0AAABAxwan/bAd9uOGJht5tn84j7Oj3XGcnQ+yetPxhVHc5tL1/POb42mcPX7oTJw9PHfqY3qZ3rSNsxtfyvs7vDmLs0uXd+Nsu5RdW297L29zdTnOzs6uxtl0fJVSN8bGpSJbMc+Wb+TPt0zycdMbZ2NhPsr7mrbJG8+8Yr5NtpbibP8gH7+DnaM4m+5r44srcZs1fW0meXa+mt+vmv7OB02cXbqe39vVLx7G2WY/z6bm2zv554/ydbc5uxln22G+Xx89vBVneweTODtfHcbZGv39vA/NJDvnzdbyvton3rxqzmWHl9bjbM07wmD7IM9ezdbp6T0bcZs1Y71/reL8VmF2MV/L9u/L959FWbmerzm9af5umRocLOZ7DDV93Xl3Ph8Gh/lZfvW5fG+vmTs10nbbUb6v1tQumvFizm53g2/QAAAAAHRMgQYAAACgYwo0AAAAAB1ToAEAAADomAINAAAAQMcUaAAAAAA6pkADAAAA0DEFGgAAAICOKdAAAAAAdEyBBgAAAKBjg9N+2A77cUOzYRNne5M8Ox/kNaTp2qmX81X9w3nc5mRzGGePL5yLszfflvW1lFL6R22cve9nn4yz81t7cbYZjfLs2c08O5nG2bjNg6M42796I293bz/Onjl/Ns4efPChODtdzufD0fl8jK0+dxxne9Ns/qS5RapZw7gzNfd4PqjYJ4b5WK/pQxu22z/I16bZaj7XZg9vxdkaNfd27dPPxtnZlWt5djKOs80w31MWoR1P8uz29kL6sHw13ydq9tX5an5uma3l2cFOvk80k1nYZpZbpHaYz99FnFneCtpRfo+r2q3YJ6ZnV+96u80kP+fUZCcP5O8Ts5X83u7fn8/3tefyNXJR572ae5aqGTM111XT1944X/eWb+TPd7yRn4WOzy/F2ZoxtvT0C3E2VbPuNhXr+cIc52ehV+MbNAAAAAAdU6ABAAAA6JgCDQAAAEDHFGgAAAAAOqZAAwAAANAxBRoAAACAjinQAAAAAHRMgQYAAACgYwo0AAAAAB1ToAEAAADo2OC0Hx6fX4ob6k/aONtM82xvOo+zcZsVfT04d+otepkX3tvE2Xt/expnz3zs6Thb5R0PxdFr33Q2zt74UP7M2qUwO8vvbY2t38mf770f380bvnw9jq785hfj7PJD98bZ6xXPbLq8EmfP/+a1KNcu5fe2Hfbj7GxlGGfLIK9BL2KteSsYX8zHznyYz+P5NH927TDPTrayfW0+yPs6vDmJs4f35fvq4CAfk6u/ne8Tsxd24mz/3otx9vixfH26+XB+H47PZs9ini85pX+UZ1ev5c9h8zM7cbZ98pk4O3/m2Tjb316Ls70H8md2fGk9zi49u5d9/kH+INphzZ7SfZYXjS+sLqTdec0eX9HuZDV7zv1xvjaMrh/E2Vvv3IizNXvVmWeO42x/P9/XZmv5uezWQ/naf3BP/tTGW9m73bziCNk/zO/tyrX83XL1aj5uVq6P4+xoN8/WGG+O4uzBYxfi7Eq6T+xkuVJKyZ9CKWVRa/9d2Cd8gwYAAACgYwo0AAAAAB1ToAEAAADomAINAAAAQMcUaAAAAAA6pkADAAAA0DEFGgAAAICOKdAAAAAAdEyBBgAAAKBjCjQAAAAAHRuc9sPl5w/ihmYrwzg7XTv1Y19mPshrSO2giXI7D+d9nYdtllLKhU/P4uyZjz0dZ8vqShy9+pH74+z1D0/j7EMPPx9nv/vcc3kfjtei3NEsf2bL/Umc3X1Xfm+f+sjZONt85h1x9sFfzudZ/+OfjbMXyqNx9vChM3H2+IHNKLd0eTdus0a/ItsO83TNWsOLlp7di7P9reU4O1vN94lmMs/7cJCte0cP5n0db+TjbPmFfN1d/sQX42w7HsfZ8v7H4uiXP5LN91JK2X8gfw6zrXyd7lrNSnbtG/J94vxntuLs1uO34uz8M1+Is73LV+LscLXi7BRmezv5deWnsVLaYc36kc/JmnZ50dLTL8TZ+VZ2LiyllMnmUpyt2SdWwn3t4G352enWh7bibI3NJ4/ibM09qLm31z6YZ8dbbZydLefZVJO/qpXZSv75t96et7v7rnw1O/NE/p6y+VS+li3dOI6zo938fHF8Ph8L0/BMONrJz5nNXv5O1a6vxtnyOq/93kgAAAAAOqZAAwAAANAxBRoAAACAjinQAAAAAHRMgQYAAACgYwo0AAAAAB1ToAEAAADomAINAAAAQMcUaAAAAAA6pkADAAAA0LHBaT/s7R3HDU02l+PsaHccZ+eDvIY0HzZRbrac5UopZeXaPM6e+cefjLPl/Nk4ev3b7o+zs+9/Ic6+a20/zi4NpnF2bzqKs0ezYZTbOVqJ29w+OBdnD/aW4ux8mo/FpfffirNX9s/E2UuHj8XZ5oln4+zSav7Mbr59NcoNDrJcKaX0DvI1oUZVuxX3gBc1k3xt6I1ncba/P6noQ95ub3z3fy/Rm7ZxduWzz8XZ2V6+Rrcf+ro4++V/PV9zDu/L98AyyO9Dc9zP2w21/YrPn+XngOFOPmZGO3m7O4/l2flgI86enzwaZ8vlK3F0cD3f18aXtqLcfCsfizVrzaKy3Jmaezwf5WvD6PpB3of9ozhbhqe+Ht2Rmn1i7bl8/6vZK2dr2Zm7lFKufmN+Pp6sV6y903zdG23H0TIPL62t2Kdm+attGezl1zU4yrNHF/P+9qY1+2r+fFeey+fZ6GY+Hg8vZOfuZpK/1w0v5+/BNZqDivXjLvANGgAAAICOKdAAAAAAdEyBBgAAAKBjCjQAAAAAHVOgAQAAAOiYAg0AAABAxxRoAAAAADqmQAMAAADQMQUaAAAAgI4p0AAAAAB0bHDaD+frS3FDw92jONvbO46z84vrcXY2bLI2h3GTZePJgzjbW1+Ls7c+/HCcfeF7DuPst1x8Ls5+9He+Ls5ufia/ab/74fyefehtz0S57YNzcZvr/3Ajzl6qeL6lzOLkC+/Nx+31D0/i7DNLm3H2kX+wH2f72xX34e2rUWy8OYqbzJOl9A7GcbY5nubZYb+iF3xFOzx1G3mZ/m6+ltWYr+Z71fGF5Sg3Xc72k1JKOf9bO3F2duVanO0/dCnOfuk7zsTZwwfztax3mN+H+z4aR8ve/fl8u/nurL/NLO/rhU/kv586/1vbcbZ3kJ+FDt95Ic5e+2C+Su6+fyvObl17Ic7Onnk2zvYuZONxupXNx1JKGexUnDMrnkMZ53twqVjveNF8Kz8TDa7ejLPNJN/ja/aq+Vq2pxxv5uvI0u48zvYP8+s6urgSZ194T36WP7rQxtnRdr72Xvrn+TlgvJH394X3ZM/3KF92y70fz5/Zxq98Ic42w/y6Zg9ejLM7787n2f69+R68dCPPNpP8nqVmK/ncHVTM82av5h2wwlLNW80r8w0aAAAAgI4p0AAAAAB0TIEGAAAAoGMKNAAAAAAdU6ABAAAA6JgCDQAAAEDHFGgAAAAAOqZAAwAAANAxBRoAAACAjinQAAAAAHRscNoPm8lsIR86O7u6kHbngybKLW23cZuDqzfzDqyvxdFrHzj11r/Mex74UpzdGef3du2Lwzi79cVJnO39icM4e2FpP8odHY7iNs/fzMftU38qf2bjC3m77/zf83twcF/+zHrfvBNnp7+8EWeb3/5cnN1aysbudGM5//yKtaYd9uMsbyzz1aU4WzMmFrFX9ab5PtHbuRVna3q6/+6LcfbofN7ftp9nV67k823l+YM4u/v2inPA2jSKDS/n+8S5T+V7+/Y3bMXZ/fvz33s98Eu7cfbs7+XtXv0j+fnizBP3xtnmhZ04O7iezYnxpa24zRrtML8HpSbLHZmt5XOzaoffP6ruS2I+ynqRvncs0tH5/I6Nt/K1f7Yyj7NLT+br0/DqXpzdv+98nJ2Ht6FfMWQ2/9lT+ec/mK+lz3/rVpy97x9+Ps6ev5a/0zzxFx6Ks8vb+Xl+5fo4zi7tZHv7wizl61KNqv3nVfgGDQAAAEDHFGgAAAAAOqZAAwAAANAxBRoAAACAjinQAAAAAHRMgQYAAACgYwo0AAAAAB1ToAEAAADomAINAAAAQMcUaAAAAAA6Njjth+2wHzc0WxnG2clmnl2EjafGefjgMI7O7zkbZ48enMTZx9avxtlPXH84zq5ca+Ps4blTh8rLfPj+p+Nsaj5r4uzxRj5u5+/In++77r0eZ/fufyDOrj6fP4f+Uj529x46E2fXP5a327+6HeXmFx6M26zRm87z7N5xnG2Op3fSHSrM1mrW/m73idWr+XhoJ/l63ozy69q7lK+7061ZnO3v5b+bOft7FfdhmLe7/2A+j9tptv73j/J9osbhxYrreiR/DnvvWI+za186iLPtIG93911rcfbs4/nYbfez/rbDc3Gb89X883v57Sq9g6M42w7zOcmLBtv5A5meXY2z7dZynO3v5+v0eHMU5QaH+fmtmebZ3jhfR8Zn8nVvXrGtNuG6W0opq1cr1vMvPRtnD/7ExTg72cjub/+w4n7d2ouz06+7FGePt/I+TN6fv9cNdvK1bJBHy3RlMXtr/zA7XxyfX8obfTjfU0bX83Wp2c9vWDN57e8TvkEDAAAA0DEFGgAAAICOKdAAAAAAdEyBBgAAAKBjCjQAAAAAHVOgAQAAAOiYAg0AAABAxxRoAAAAADqmQAMAAADQscFpP5xsLscNTTb6r7kzr9juSl5D6k3bKDc4mMRtzm/t5dlH7o2zvZVpnL02Xo+zzzxxMc7ek9+Gcny2ibO/8LEPxtnH3ns5yvX62bMtpZSlm7M4Oz1ezLidD/L7VWPUz6/t8Ew+dwYPPZh3YnjqsvFVs4q5Ox/m92u0O46zNebrSwtp92vdfHUYZ2er2dgppW4O1Yyf3iRbS3qTedxmGeeLabO+FmcnZ/LramZ5dv3pfG6e+Z3n4+xsM7+2ez4xirPb78nGWJNvq6WZ5GvpoozX8+eQ39nKPlSMsd7ZrTg7396Jck3NPFuQNtzTuHPtaDH3eD7I59Dk4spC+pCqObvUrE81+0Qp+Vn6zBP5vV1/+mac7Z3J32nu+4283avffCbKTbJYKaWUtmZvX9Batn9ffjbd2K/ob8V+uah3mv5+Nidm9+f1iGb6tfHdk6+NqwAAAAB4E1OgAQAAAOiYAg0AAABAxxRoAAAAADqmQAMAAADQMQUaAAAAgI4p0AAAAAB0TIEGAAAAoGMKNAAAAAAdU6ABAAAA6NjgtB/OVvL6zaQiu3plEmeXro/j7HTt1Mv5qt5B3mYzGsXZmnZ7/eU4+8nnH4yzl365ibPjM3G0HG/l2Xf81HGc/fK3vy0Lvv8gbrOZtnG2TPJxezgdxtn5IH8Og8O8v+NZv6IPcbTOZHrXm+xN8nvQ387HQruU34TjCytxlhfN1hY1L2Zxtn8931Pmo3wOLcQ472uVaX5vl7fnC+nC+GI+h7YevxVnl19YjXI33pePxRrN3V/ySimlTCuWnN5BPm6airGwsH1iAWrm7uB6Pr6q+rCan9140WwtP0u3w/xc1j/MJ+eo4n0i7W9//CaaQKWUtmL7q9knevv5uX/y6H1xtv+p34+z9395Pcpd+d5H4zbbST5mFmW6UrGeV6yRvYqjSK/ivWo+yOdvO8z6W3N2bCuyvZ29vN29/Thb1tfy7KvwDRoAAACAjinQAAAAAHRMgQYAAACgYwo0AAAAAB1ToAEAAADomAINAAAAQMcUaAAAAAA6pkADAAAA0DEFGgAAAICOKdAAAAAAdGxw2g/7h/O4od5qXuuZreTZ3ngWZwf70yg33ViO2xzGyVJ6e0dxdnq8Fmcng36c3frtK3H2Cz94f5x997c+EWe/OH00zs5PHYEvmt1Yitsc7R7G2d7+apx9/7nn4uxvnMnv7eq1fJ4dTvOxMMxvQ5k9fzXO9u+7J8o10zZuc7ibz535ej4WnvmuzTg7+4ZbcZYXDa8dxNn5pfU8O8z3iXxW5HvKbC1f/dvxuKIHuaXtfA7tP5C3u3457+/+uy/G2cvfHi7opZTVK6M4e/bzkyg33Mvv13w1f77L2/kafTNOljIfNBXpXC87CpVSShndqrhn2ztxthllz7etmOeDneM4O9vMz1hXP7wRZ7c/mJ9JedHw8gtxdvLAuThbM35ma/maM7iazeTZw3lfZyv5+ljz7tNUzPcaW5/bi7PHl/I59Py/kp/hNh/6+ji79ekbUW798mJuWH8/31fnw3x9St+TSqmbD28E6TlrXvEyPrqZnRdKKaUcV5yFvvWdcfbKN9ecSl/Zm+tJAgAAAHwNUqABAAAA6JgCDQAAAEDHFGgAAAAAOqZAAwAAANAxBRoAAACAjinQAAAAAHRMgQYAAACgYwo0AAAAAB1ToAEAAADo2ODUHx5M8pbOn9rUy8yHTZ4d9eNs2t/j80txm6Ozm3G2xvKTeR/Gj83j7MFjF/I+XM+fw+9evi/Oru/E0TLeynLt+jRu8/C+5Th75sm8Rvnzyx+Isw8/nc+d3YeHcXY6y+fD2cvjONuM8j7Mz65Hud6kjducreSf/6XvWomz3/snPxZn/9b9/yLOlvJXK7Jf23oHRxXpbOyUUsp0JZ+b/YN8XvT3K/a1ULO+FmfnL+zE2Y2nj+Pszbfn6954I9+vRzfztXf5Rj6P157L97XeNFtLJuv5nrb/ttU4u16xlpbfGMXRzd+7FWePL+Vzp8b6s/l8aMd5tmZOLMLlP5Gf3R74nqfj7D9+509V9OIvV2S/trV7+xXpc3FyPsj3iUX8Rno2ylttB/n61D/M97TVq/laOlvK+zvZzN9TetO8D8O9OFpWrlesOeFZZLqa34PV9zyWf/5OfmFnP5+vT1ufvhFnyyTfr2f/an6WHjybn+drNJNs3Czt5uOr5oz33J95NM5Ov2snzv7EB/5+nC3lP3nFP/UNGgAAAICOKdAAAAAAdEyBBgAAAKBjCjQAAAAAHVOgAQAAAOiYAg0AAABAxxRoAAAAADqmQAMAAADQMQUaAAAAgI4p0AAAAAB0bHDaD+eDvH4zPpNn1y9P4uzo+n6cna+Ootz+vade9svbHN4TZ9d/9Qtx9tKvn4+zT17MrquUUp7+3jha3vYL+XNY+adHcfbo/vz+PvX92bj5nvc/Hrf58+V9cfbCR/tx9l0/md+D6x9Yi7M3v+Uwzva+sBlnly5fi7PNubNxdv+hM1mb0zZu8+YjK3H2L/3A/xtn//ja5+LswTwfC+txkpearuT7RE12bSefm81kmuXWhnGb7cVzcba8sBNHR8/ejLNLO8txdu9SPtbP3xjH2Yd+9mqcnW3mc377vdmM239wHrc5H1Tcg0m+lp37xPU4O760EWevfTA/B6w+l/d35ffz/s7iZCllK7u2ZpI/s+nWUpxtvm07zv6Nt/9MnH3bwOq/aLOV/Aw5Xc3n8eqXDu6kO6fqjyvGb8WaU2NpN+/D0dl8X919e77mnPtsfm8f+Okvxtkak0fvi3I1+990JX9X23giP8uf/djlODvfytec7W/M+9vPu1sGB/kY603z7HgzG2M17xPzUf58D//4Xpz9Ox/4+3H2w8uvfa77Bg0AAABAxxRoAAAAADqmQAMAAADQMQUaAAAAgI4p0AAAAAB0TIEGAAAAoGMKNAAAAAAdU6ABAAAA6JgCDQAAAEDHFGgAAAAAOjY47Yf9w0lFUyuvsSuvn9GteZydDZs4247HcXbpk0/E2fvPvyvO3vizB3H26T+9HGeHN87G2dmDR3H2m9/+ZJTbm47yNh97Ks7+/vkLcfbp7877sLV+Lc72P3Uxzj70T/Mx1n75uTg7f/cjcXaymtV11585jNucviO/t3/6zONx9p8fPhRnf+5WPhZ++FIc5SXmFetpb9oupA/NZBrl+vv5/je5uBpnR5fX4mzNHL7vn+V78DPfdSbPfmd+bcvX8+x4K46Wo/tmUa7t52Pm8ME8++X78t9l9b8lX8/bQd6HtctxtJz/9F6cnV/J96reua283dWlKDe8lvf16OH887/9gfyMdWuen4X+4V4+d/58nOSletP8jD7Ij7xV0n1idL2iAxfy9bHG0o3jOHvxZr6vffk78j1l/1K+r61cezTO1picyc4X837e5vHZfO3ffTS/B4OjPDvNl6cyyF+/ytqz+TwbHmTzoZRS5oP8nk3T94mn9/PPH+UP+F97W75P1PjZ/Xyuf/+r/Llv0AAAAAB0TIEGAAAAoGMKNAAAAAAdU6ABAAAA6JgCDQAAAEDHFGgAAAAAOqZAAwAAANAxBRoAAACAjinQAAAAAHRMgQYAAACgY4PTfthMZnlDh+1r7swr9uF4GmfTatPqc3ld6vjCKM7Ov/4dcbb/+Wfi7NZHn8r7MHh7nL3yrfkzu++PPB9n/9Slz8TZx/fuj3JXDjbiNjeXDuPsg5u7cfbJ2bk4e+MzF/M+/Mokzg4/+jtxtnffPXH2aHM578PBPMrVrB/L21mbpZTyI5f/ZJxdH4zj7GdeyMZiKaX88Pvi6Ne8dv+g6y6UZpLvE2l/+xWff3T/apxt3vdwnB0+/nTe7ic/H2ffNnkszj73kc04e/Od+Twum/m6106bLDjN9/a2X3Fmqci2k7wPG18Mr6uUcu5zR3G29/gTcbYd58+ht5aP81TN3O0f5Nlfu/xonD0/2ouzn9p5MM7++XfGUV5isJOP9XaYr9TNfkW7e/tRrua33L2t/Jx1fH4pzi7dOI6z/f18vt/7iVNfEV/mxtcP4+xBftQqs/yWlWaWrdO9/BaUeX5Zpa04NIzP5ntKzTvr2rP5HnzmmXzcNJO83dlqPm5Wrmdn9P6Xr8Vt9s7m74u/+kS+SF8c3Yqzn7t1b5z9/lcpHfgGDQAAAEDHFGgAAAAAOqZAAwAAANAxBRoAAACAjinQAAAAAHRMgQYAAACgYwo0AAAAAB1ToAEAAADomAINAAAAQMcUaAAAAAA6Njjth+2wHzc0XWnibDvIs83BUUU2yw2Pp3GbpazHyfHmKM4uPXopzjafeyrOnv2Fz8fZcx/fjLPXv+3+OPu3/+iFODtYn8TZ1PQ4H7fLTy7F2Uu/fpy3+9kn4uz81l6c7d13T5zd+fADcbY/aePs2hM3o1xvL5+7G0/mz+xjv/K+OLv07t04u7ezEmd5UTMcxtl5xdo/P3V3+gN9mORrejsO15xhvjYtPxduPqWU6Va+5sweezDO9r/w5bzdTz4eZx94Nl9zJo/l+9qtR5bz7EPZ75LaijFTY7STZy98Jl/3lr5wJc7OrlyLs/lqXkr/ofyZTS+cydvdPcyC6XwspQx28ns7/e1zcfanpx/K2536veaizdbys3Q7zJ9H/1q+T8xuZueymtEwvJwvUM1kI87OVvJ2a+7XyrP52fSBa/kZbu/htTh7cE/e34PwNWWeH1lKm19WGW3n55szz1ScuZ/L3z2Gu3m2xmQzP7f0x/M4O7yajbF2ku8TNXWD9V/L31d/avaNcXZrcz/Ovho7DQAAAEDHFGgAAAAAOqZAAwAAANAxBRoAAACAjinQAAAAAHRMgQYAAACgYwo0AAAAAB1ToAEAAADomAINAAAAQMcUaAAAAAA6Njjth5PN5bih+aktvQaTSZ4dDqNYM5nGTfam8zi78tROnD18ZCvOzr7zfXF2/XdfiLPzLz8XZ8//3G6cPfepe+Nsc5w/i1S7lA/G3vaVODt/YTvOzsb5uG3e82icPbh/Pc6Obs7i7NL1wzibmq/n60eNh/+fvK87X9iMs6sVS035CxXZr3Ht2spC2h0ctXm4Yr6143EWHGX7SSmlNJOKufZ0vkZPL5yJs5P3PRxnh4/H0TK7cjXO9iqyWx8fxdmz4bNoRnmbNeIxU0qZ7+/n2bW1ONtbz7PlnvNxdLqZz9/eQc0imalZP9phP84+9Iu34uzuk/m+WuXPLabZN6OmZvxWmA+6/T1ze3QcZ3sV7x6Dzz8TZ/sP5mfuW+/ciLM1O3szqXhXup6vpyvX8z6c/b1sLNS8101W8/eJ/jhvt3+Yj4V2mI/x2Vp+bqmZO7NRnl39QsVDC9WsH+0wf2b3/8wTcfbMl/Mz1u4jF+Js+VOv/Me+QQMAAADQMQUaAAAAgI4p0AAAAAB0TIEGAAAAoGMKNAAAAAAdU6ABAAAA6JgCDQAAAEDHFGgAAAAAOqZAAwAAANAxBRoAAACAjg26+ND+4TzOtsfjONusrmTByTRus3eQf36N0W7e7vFgKc4ePrIVZwcX1+Ns/3CSZ7f342ys4pk1BxXtDvMp0Dx4f5xt1/NndnQhHLellHbQxNmVZ27F2eY4v7/zimtbhMHNozh7/lP5uG0mszvpDgvSm7Rxtp1UPOf1tSw4ztvsHeRjsqbdwbMvxNnppXNxdv7IpTjbe+DePFtxH+ZXrsXZuM29Bew9pZRmNIyz/XvvibPzSxfzTixofervHi6k3VRbsQfXqLmuc5/Is03FWYSXqDjLz1Y6eTV5md5Gdj6e39zLG615nxnma05zJd8nNsYVZ721/Kw3W8v7O97Is6Ob+X65CMOD7ud7zf0aHOT7xOh6/rLU7Od7+yLWyKp9oiLbrq/G2bXfzc8s65+uuAf/3Sv/sW/QAAAAAHRMgQYAAACgYwo0AAAAAB1ToAEAAADomAINAAAAQMcUaAAAAAA6pkADAAAA0DEFGgAAAICOKdAAAAAAdGxw2g9741nc0HzQ5Nlhnl2I4amXfcfm60sLaXfpxnGc7R2M42w77MfZg4fW4uzxezfibG/aRrnh4Txuc7ag8TW6mc+HpeuHcXblqZ076M0frl3Kx/kixm4zye9XTbZm3C6qXV7UTKZxdl6x9E5XOv79wWjY7edXGly/1XUXyvTCmTg7v7S1uI50qL8/ibO9g3xvr5lnZZz3oWactws4O9VcV9U9WFAfuDPtpGJMVpiu5vt2zfht1rMzb80uNb+1l3/+cn4ma4b5HG62b8bZ/nYcLf2lUd6HB87F2dlK/sxmo+xppLlSSmkr3m1r1LzXrX4p39ub/aM8u3cQZ6tUjIVU1Rpdka1aE2r6cJy/i78a36ABAAAA6JgCDQAAAEDHFGgAAAAAOqZAAwAAANAxBRoAAACAjinQAAAAAHRMgQYAAACgYwo0AAAAAB1ToAEAAADomAINAAAAQMcGd6uhtqKl8UY/zq6ur+V9GGadaCbTuM3e3lGcna8vx9kazWSWZ48rrm17L86uxslSykP5M1u6Pq5pOTIf5eNruJs/397ecZxtDvJ2q4RjvFbNuJmvL931Nmu0w/z51qiZZyzedLmJs81wGGfbySQLjsNcKaUtB3G2WatYTWv6sJ/3oUpFHwYV2fEjF+Ps8Fq+Vy1CzZlhUc+hrbi3ZZTPh3yWldLUjMe1lazNinv7hlDzHPiqmjW6xmyUj+B2LT+jN1deuJPunP75FWOnd2b9rn9+KRX7X6X5je0427+e39vB2y7F2YWdu1PH+ftMzXNoj/J3j3nFGGtq9onl7NxfSt2e0q5n56GqfaLiOdT0tcbdmGe+QQMAAADQMQUaAAAAgI4p0AAAAAB0TIEGAAAAoGMKNAAAAAAdU6ABAAAA6JgCDQAAAEDHFGgAAAAAOqZAAwAAANAxBRoAAACAjg1O+2FvOo8bWtpu42x/kmdrNJPpQtpdhGYy67oLVWr6e3ymH2f7h6cOwTeUqvE1XMx1tYtqdylvtx1mz3d2djVus2Z81WR7e0dxdlH3lheNbuV7ynSl498fjIaLaXc8iaPtJM++2cxW8/nW21yJcs0kH181+rsLOltUjIVFWdwYy55ZzbpbtQfX3NtFzXW+qmacDXeP4+xstJh9Iu3v/ObeQj6/Rs29bYb5WH8j7D+Te9bj7GAnW0ua/fxcWKM5HufZmudwlM+HpmItaxe0/9RcW6pqn6h4Du3eft5uxXXdjXvgGzQAAAAAHVOgAQAAAOiYAg0AAABAxxRoAAAAADqmQAMAAADQMQUaAAAAgI4p0AAAAAB0TIEGAAAAoGMKNAAAAAAdU6ABAAAA6NjgtB82k1nc0MoL09fcmVc0PLWLL9OG2WaymL42x3m77VJ+XVV9qLm2ins7Xx3l2WHehfFGP8oND+Z5oxVmK3lne3sVY7Hi+b4Rxk2NdF1oh9mz5a1j6cY4zvY2KhaSr1HNML8H7WSSNzyuyNYY5f2dD5o4O1vL2u3v59c1H+XrUzNZzrP7B3l2bTXOthXt1ljYGNu5edc/v2Z8LcwboQ9vQjXPubezH2f7m0t30p1ONBVjp2auVc2hCu3Rcd6HimtrlhfzzGZr2XvKG+FkOn/6cpztbawvpA9txTlgUc+sbO9mn18zxpfy99VSs6e9znyDBgAAAKBjCjQAAAAAHVOgAQAAAOiYAg0AAABAxxRoAAAAADqmQAMAAADQMQUaAAAAgI4p0AAAAAB0TIEGAAAAoGMKNAAAAAAdG5z2w2Yyixsa7k7i7PGFUZydry/H2UVoJtM42y6dejtfnh3242x/ez/OloPDPDsc5tkK8/w2lPmgiXLNtI3bbMM2a9WMhfn60kL6UKNmjNVIx2PN3O3tHcXZdpgPsK7Xj7eEcb7298b5njJdrZhDo4q1bJL1t1lbjZts9w/yz1+Qdi/fJ5pRvgcvSq9iTe9azVraW9C+WjMeF2ZrI442+9lZpA3nYyklnrullNIs6DnwOqjY44+38mz/MF/30lZ7G+txm+3RcZytUTOHavrQVuztNZrlbs/HzbjiLL+W97WpGLdNxZmlai1bysf4wr6hUdGH9G2tZozXvAG+kfcJ36ABAAAA6JgCDQAAAEDHFGgAAAAAOqZAAwAAANAxBRoAAACAjinQAAAAAHRMgQYAAACgYwo0AAAAAB1ToAEAAADomAINAAAAQMeatm277gMAAADAW5pv0AAAAAB0TIEGAAAAoGMKNAAAAAAdU6ABAAAA6JgCDQAAAEDHFGgAAAAAOvb/A9Ax48ZhzzRUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def Wasserstein_Dist(XX, YY):\n",
    "  \n",
    "    import numpy as np\n",
    "    nx = len(XX)\n",
    "    ny = len(YY)\n",
    "    n = nx + ny\n",
    "\n",
    "    XY = np.concatenate([XX,YY])\n",
    "    X2 = np.concatenate([np.repeat(1/nx, nx), np.repeat(0, ny)])\n",
    "    Y2 = np.concatenate([np.repeat(0, nx), np.repeat(1/ny, ny)])\n",
    "\n",
    "    S_Ind = np.argsort(XY)\n",
    "    XY_Sorted = XY[S_Ind]\n",
    "    X2_Sorted = X2[S_Ind]\n",
    "    Y2_Sorted = Y2[S_Ind]\n",
    "\n",
    "    Res = 0\n",
    "    E_CDF = 0\n",
    "    F_CDF = 0\n",
    "    power = 1\n",
    "\n",
    "    for ii in range(0, n-2):\n",
    "        E_CDF = E_CDF + X2_Sorted[ii]\n",
    "        F_CDF = F_CDF + Y2_Sorted[ii]\n",
    "        height = abs(F_CDF-E_CDF)\n",
    "        width = XY_Sorted[ii+1] - XY_Sorted[ii]\n",
    "        Res = Res + (height ** power) * width;  \n",
    " \n",
    "    return Res\n",
    "\n",
    "def  Wasserstein_Dist_PVal(XX, YY):\n",
    "    # Information about Bootstrap: https://towardsdatascience.com/an-introduction-to-the-bootstrap-method-58bcb51b4d60\n",
    "    import random\n",
    "    nboots = 1000\n",
    "    WD = Wasserstein_Dist(XX,YY)\n",
    "    na = len(XX)\n",
    "    nb = len(YY)\n",
    "    n = na + nb\n",
    "    comb = np.concatenate([XX,YY])\n",
    "    reps = 0\n",
    "    bigger = 0\n",
    "    for ii in range(1, nboots):\n",
    "        e = random.sample(range(n), na)\n",
    "        f = random.sample(range(n), nb)\n",
    "        boost_WD = Wasserstein_Dist(comb[e],comb[f]);\n",
    "        if (boost_WD > WD):\n",
    "            bigger = 1 + bigger\n",
    "            \n",
    "    pVal = bigger/nboots;\n",
    "\n",
    "    return pVal, WD\n",
    "\n",
    "C_num = 3\n",
    "\n",
    "# Separating Wrong Responses of the CNN Classifier\n",
    "X_test_wrong, y_test_wrong = X_test[np.where(y_test != y_pred)], y_test[np.where(y_test != y_pred)]\n",
    "\n",
    "# print(X_test_wrong.shape)\n",
    "\n",
    "# Finding Wrong Decisions for Label 2 (just an example)\n",
    "X_test_wrong3, y_test_wrong3 = X_test_wrong[np.where(y_test_wrong == C_num)], y_test_wrong[np.where(y_test_wrong == C_num)]\n",
    "\n",
    "# print(X_test_wrong1.shape)\n",
    "\n",
    "X_train3 = X_train[np.where(y_train[:,C_num] == 1)]\n",
    "\n",
    "fig4, ax44 = pyplot.subplots(1,3, figsize = (20,6))\n",
    "#fig5, ax55 = pyplot.subplots(1,3, figsize = (20,6))\n",
    "\n",
    "for ii, c_ax44 in enumerate(ax44.flatten()):\n",
    "    # Comparing X_train for Label == 2 with X_Test_wronge for Label == 2\n",
    "    xxx, yyy= X_train3[:,:,:,ii], X_test_wrong3[:,:,:,ii]\n",
    "    xxx_2   = np.array([yy.flatten() for yy in xxx])\n",
    "    yyy_2   = np.array([yy.flatten() for yy in yyy]) \n",
    "    \n",
    "    WD = np.zeros(900)\n",
    "    pVal = np.zeros(900)\n",
    "      \n",
    "    for kk in range(1, 900):\n",
    "        WD[kk], pVal[kk] = Wasserstein_Dist_PVal(xxx_2[:30,kk], yyy_2[:30,kk]) \n",
    "        \n",
    "    WD2 = WD    \n",
    "\n",
    "    print(1 - WD2[WD2 != 0].mean())\n",
    "    #print(1 - WD2.mean())\n",
    "    \n",
    "    WD3 = WD\n",
    "    WD3[pVal > 0.05] = 0\n",
    "    \n",
    "    J,Q     = xxx_2.shape\n",
    "    z       = WD3\n",
    "    zstar   = pVal #WD.mean()\n",
    "    z0      = np.zeros(Q)\n",
    "    z0      = z\n",
    "    z1      = z0.copy()\n",
    "    #z1[np.abs(z1)<zstar] = 0\n",
    "    Z0      = np.reshape(z1, (30,30))\n",
    "    #Z0i     = Z0.copy()\n",
    "    #Z0i[np.abs(Z0i)<zstar] = 0\n",
    "    #ZZ      = np.hstack( [Z0, Z0i] )\n",
    "    \n",
    "    z2      = WD\n",
    "    Z02      = np.reshape(z2, (30,30))\n",
    "    \n",
    "    c_ax44.imshow(Z0, 'jet') # Can be replaced with Z0i\n",
    "    c_ax44.set_title('RGB: {} of Set {} vs. Set {}'.format(ii, 1,2))\n",
    "    c_ax44.axis('off')\n",
    "    \n",
    "fig5, ax55 = pyplot.subplots(1,3, figsize = (20,6))     \n",
    "\n",
    "for ii, c_ax55 in enumerate(ax55.flatten()):\n",
    "    c_ax55.imshow(X_train3[1,:,:,ii], interpolation = 'none')\n",
    "    c_ax55.set_title('RGB: {} -- Test {}'.format(ii+1, 3))\n",
    "    c_ax55.axis(\"off\")\n",
    "    \n",
    "fig4, ax8 = pyplot.subplots(1,3, figsize = (20,6))     \n",
    "\n",
    "for ii, c_ax in enumerate(ax8.flatten()):\n",
    "    c_ax.imshow(X_test_wrong3[1,:,:,ii], interpolation = 'none')\n",
    "    c_ax.set_title('RGB: {} -- Test {}'.format(ii+1, y_test_wrong3[1]))\n",
    "    c_ax.axis('off')    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Anderson-Darling Distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9071465968586387\n",
      "0.9172134387351779\n",
      "0.9307662835249042\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def Anderson_Darling_Dist(XX, YY):\n",
    "  \n",
    "    import numpy as np\n",
    "    nx = len(XX)\n",
    "    ny = len(YY)\n",
    "    n = nx + ny\n",
    "\n",
    "    XY = np.concatenate([XX,YY])\n",
    "    X2 = np.concatenate([np.repeat(1/nx, nx), np.repeat(0, ny)])\n",
    "    Y2 = np.concatenate([np.repeat(0, nx), np.repeat(1/ny, ny)])\n",
    "\n",
    "    S_Ind = np.argsort(XY)\n",
    "    XY_Sorted = XY[S_Ind]\n",
    "    X2_Sorted = X2[S_Ind]\n",
    "    Y2_Sorted = Y2[S_Ind]\n",
    "\n",
    "    Res = 0\n",
    "    E_CDF = 0\n",
    "    F_CDF = 0\n",
    "    G_CDF = 0\n",
    "    height = 0\n",
    "    SD = 0\n",
    "    power = 1\n",
    "\n",
    "    for ii in range(0, n-2):\n",
    "        E_CDF = E_CDF + X2_Sorted[ii]\n",
    "        F_CDF = F_CDF + Y2_Sorted[ii]\n",
    "        G_CDF = G_CDF + 1/n\n",
    "        SD = (n * G_CDF * (1-G_CDF))**0.5\n",
    "        height = abs(F_CDF - E_CDF)\n",
    "        if XY_Sorted[ii+1] != XY_Sorted[ii]: \n",
    "            if SD>0:\n",
    "                Res = Res + (height/SD)\n",
    "    AD_Dist = Res  \n",
    "    \n",
    "    return AD_Dist\n",
    "\n",
    "def  Anderson_Darling_Dist_PVal(XX, YY):\n",
    "    import random\n",
    "    nboots = 1000\n",
    "    ADD = Anderson_Darling_Dist(XX,YY)\n",
    "    na = len(XX)\n",
    "    nb = len(YY)\n",
    "    n = na + nb\n",
    "    comb = np.concatenate([XX,YY])\n",
    "    reps = 0\n",
    "    bigger = 0\n",
    "    for ii in range(1, nboots):\n",
    "        e = random.sample(range(n), na)\n",
    "        f = random.sample(range(n), nb)\n",
    "        boost_ADD = Anderson_Darling_Dist(comb[e],comb[f]);\n",
    "        if (boost_ADD > ADD):\n",
    "            bigger = 1 + bigger\n",
    "            \n",
    "    pVal = bigger/nboots;\n",
    "\n",
    "    return pVal, ADD\n",
    "\n",
    "C_num = 3\n",
    "\n",
    "# Separating Wrong Responses of the CNN Classifier\n",
    "X_test_wrong, y_test_wrong = X_test[np.where(y_test != y_pred)], y_test[np.where(y_test != y_pred)]\n",
    "\n",
    "# print(X_test_wrong.shape)\n",
    "\n",
    "# Finding Wrong Decisions for Label 2 (just an example)\n",
    "X_test_wrong3, y_test_wrong3 = X_test_wrong[np.where(y_test_wrong == C_num)], y_test_wrong[np.where(y_test_wrong == C_num)]\n",
    "\n",
    "# print(X_test_wrong1.shape)\n",
    "\n",
    "X_train3 = X_train[np.where(y_train[:,C_num] == 1)]\n",
    "\n",
    "fig4, ax44 = pyplot.subplots(1,3, figsize = (20,6))\n",
    "#fig5, ax55 = pyplot.subplots(1,3, figsize = (20,6))\n",
    "\n",
    "for ii, c_ax44 in enumerate(ax44.flatten()):\n",
    "    # Comparing X_train for Label == 2 with X_Test_wronge for Label == 2\n",
    "    xxx, yyy= X_train3[:,:,:,ii], X_test_wrong3[:,:,:,ii]\n",
    "    xxx_2   = np.array([yy.flatten() for yy in xxx])\n",
    "    yyy_2   = np.array([yy.flatten() for yy in yyy]) \n",
    "    \n",
    "    ADD = np.zeros(900)\n",
    "    pVal = np.zeros(900)\n",
    "    \n",
    "      \n",
    "    for kk in range(1, 900):\n",
    "        ADD[kk], pVal[kk] = Anderson_Darling_Dist_PVal(xxx_2[:30,kk], yyy_2[:30,kk]) \n",
    "        \n",
    "    ADD2 = ADD    \n",
    "\n",
    "    print(1 - ADD2[ADD2 != 0].mean())\n",
    "    \n",
    "    ADD2[pVal > 0.05] = 0\n",
    "    \n",
    "    J,Q     = xxx_2.shape\n",
    "    z       = ADD2\n",
    "    zstar   = pVal #WD.mean()\n",
    "    z0      = np.zeros(Q)\n",
    "    z0      = z\n",
    "    z1      = z0.copy()\n",
    "    #z1[np.abs(z1)<zstar] = 0\n",
    "    Z0      = np.reshape(z1, (30,30))\n",
    "    #Z0i     = Z0.copy()\n",
    "    #Z0i[np.abs(Z0i)<zstar] = 0\n",
    "    #ZZ      = np.hstack( [Z0, Z0i] )\n",
    "    \n",
    "    z2      = ADD\n",
    "    Z02     = np.reshape(z2, (30,30))\n",
    "    \n",
    "    c_ax44.imshow(Z0, 'jet') # Can be replaced with Z0i\n",
    "    c_ax44.set_title('RGB: {} of Set {} vs. Set {}'.format(ii, 1,2))\n",
    "    c_ax44.axis('off')\n",
    "    \n",
    "fig5, ax55 = pyplot.subplots(1,3, figsize = (20,6))     \n",
    "\n",
    "for ii, c_ax55 in enumerate(ax55.flatten()):\n",
    "    c_ax55.imshow(X_train3[1,:,:,ii], interpolation = 'none')\n",
    "    c_ax55.set_title('RGB: {} -- Test {}'.format(ii+1, 3))\n",
    "    c_ax55.axis(\"off\")\n",
    "    \n",
    "fig4, ax8 = pyplot.subplots(1,3, figsize = (20,6))     \n",
    "\n",
    "for ii, c_ax in enumerate(ax8.flatten()):\n",
    "    c_ax.imshow(X_test_wrong3[1,:,:,ii], interpolation = 'none')\n",
    "    c_ax.set_title('RGB: {} -- Test {}'.format(ii+1, y_test_wrong3[1]))\n",
    "    c_ax.axis('off')  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9325805921052631\n",
      "0.9311991814461119\n",
      "0.9425293333333333\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Cramer-Von Mises Distance\n",
    "def CVM_Dist(XX, YY):\n",
    "  \n",
    "    import numpy as np\n",
    "    nx = len(XX)\n",
    "    ny = len(YY)\n",
    "    n = nx + ny\n",
    "\n",
    "    XY = np.concatenate([XX,YY])\n",
    "    X2 = np.concatenate([np.repeat(1/nx, nx), np.repeat(0, ny)])\n",
    "    Y2 = np.concatenate([np.repeat(0, nx), np.repeat(1/ny, ny)])\n",
    "\n",
    "    S_Ind = np.argsort(XY)\n",
    "    XY_Sorted = XY[S_Ind]\n",
    "    X2_Sorted = X2[S_Ind]\n",
    "    Y2_Sorted = Y2[S_Ind]\n",
    "\n",
    "    Res = 0;\n",
    "    E_CDF = 0;\n",
    "    F_CDF = 0;\n",
    "    power = 1;\n",
    "\n",
    "    for ii in range(0, n-2):\n",
    "        E_CDF = E_CDF + X2_Sorted[ii]\n",
    "        F_CDF = F_CDF + Y2_Sorted[ii]\n",
    "        height = abs(F_CDF - E_CDF)\n",
    "        if XY_Sorted[ii+1] != XY_Sorted[ii]: Res = Res + height**power\n",
    "\n",
    "    CVM_Dist = Res\n",
    "    \n",
    "    return CVM_Dist\n",
    "\n",
    "def  CVM_Dist_PVal(XX, YY):\n",
    "    import random\n",
    "    nboots = 1000\n",
    "    CVM = CVM_Dist(XX,YY)\n",
    "    na = len(XX)\n",
    "    nb = len(YY)\n",
    "    n = na + nb\n",
    "    comb = np.concatenate([XX,YY])\n",
    "    reps = 0\n",
    "    bigger = 0\n",
    "    for ii in range(1, nboots):\n",
    "        e = random.sample(range(n), na)\n",
    "        f = random.sample(range(n), nb)\n",
    "        boost_CVM = CVM_Dist(comb[e],comb[f]);\n",
    "        if (boost_CVM > CVM):\n",
    "            bigger = 1 + bigger\n",
    "            \n",
    "    pVal = bigger/nboots;\n",
    "\n",
    "    return pVal, CVM\n",
    "\n",
    "C_num = 3\n",
    "\n",
    "# Separating Wrong Responses of the CNN Classifier\n",
    "X_test_wrong, y_test_wrong = X_test[np.where(y_test != y_pred)], y_test[np.where(y_test != y_pred)]\n",
    "\n",
    "# print(X_test_wrong.shape)\n",
    "\n",
    "# Finding Wrong Decisions for Label 2 (just an example)\n",
    "X_test_wrong3, y_test_wrong3 = X_test_wrong[np.where(y_test_wrong == C_num)], y_test_wrong[np.where(y_test_wrong == C_num)]\n",
    "\n",
    "# print(X_test_wrong1.shape)\n",
    "\n",
    "X_train3 = X_train[np.where(y_train[:,C_num] == 1)]\n",
    "\n",
    "fig4, ax44 = pyplot.subplots(1,3, figsize = (20,6))\n",
    "#fig5, ax55 = pyplot.subplots(1,3, figsize = (20,6))\n",
    "\n",
    "for ii, c_ax44 in enumerate(ax44.flatten()):\n",
    "    # Comparing X_train for Label == 2 with X_Test_wronge for Label == 2\n",
    "    xxx, yyy= X_train3[:,:,:,ii], X_test_wrong3[:,:,:,ii]\n",
    "    xxx_2   = np.array([yy.flatten() for yy in xxx])\n",
    "    yyy_2   = np.array([yy.flatten() for yy in yyy]) \n",
    "    \n",
    "    CVM = np.zeros(900)\n",
    "    pVal = np.zeros(900)\n",
    "    \n",
    "      \n",
    "    for kk in range(1, 900):\n",
    "        CVM[kk], pVal[kk] = CVM_Dist_PVal(xxx_2[:30,kk], yyy_2[:30,kk]) \n",
    "        \n",
    "    CVM2 = CVM   \n",
    "\n",
    "    print(1 - CVM2[CVM2 != 0].mean())\n",
    "    \n",
    "    CVM2[pVal > 0.05] = 0\n",
    "    \n",
    "    J,Q     = xxx_2.shape\n",
    "    z       = CVM2\n",
    "    zstar   = pVal #WD.mean()\n",
    "    z0      = np.zeros(Q)\n",
    "    z0      = z\n",
    "    z1      = z0.copy()\n",
    "    #z1[np.abs(z1)<zstar] = 0\n",
    "    Z0      = np.reshape(z1, (30,30))\n",
    "    #Z0i     = Z0.copy()\n",
    "    #Z0i[np.abs(Z0i)<zstar] = 0\n",
    "    #ZZ      = np.hstack( [Z0, Z0i] )\n",
    "    \n",
    "    z2      = CVM\n",
    "    Z02     = np.reshape(z2, (30,30))\n",
    "    \n",
    "    c_ax44.imshow(Z0, 'jet') # Can be replaced with Z0i\n",
    "    c_ax44.set_title('RGB: {} of Set {} vs. Set {}'.format(ii, 1,2))\n",
    "    c_ax44.axis('off')\n",
    "    \n",
    "fig5, ax55 = pyplot.subplots(1,3, figsize = (20,6))     \n",
    "\n",
    "for ii, c_ax55 in enumerate(ax55.flatten()):\n",
    "    c_ax55.imshow(X_train3[1,:,:,ii], interpolation = 'none')\n",
    "    c_ax55.set_title('RGB: {} -- Test {}'.format(ii+1, 3))\n",
    "    c_ax55.axis(\"off\")\n",
    "    \n",
    "fig4, ax8 = pyplot.subplots(1,3, figsize = (20,6))     \n",
    "\n",
    "for ii, c_ax in enumerate(ax8.flatten()):\n",
    "    c_ax.imshow(X_test_wrong3[1,:,:,ii], interpolation = 'none')\n",
    "    c_ax.set_title('RGB: {} -- Test {}'.format(ii+1, y_test_wrong3[1]))\n",
    "    c_ax.axis('off')  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## KUIPER\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9322259136212625\n",
      "0.9314884667571235\n",
      "0.9429585561497327\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def Kuiper_Dist(XX, YY):\n",
    "  \n",
    "    import numpy as np\n",
    "    nx = len(XX)\n",
    "    ny = len(YY)\n",
    "    n = nx + ny\n",
    "\n",
    "    XY = np.concatenate([XX,YY])\n",
    "    X2 = np.concatenate([np.repeat(1/nx, nx), np.repeat(0, ny)])\n",
    "    Y2 = np.concatenate([np.repeat(0, nx), np.repeat(1/ny, ny)])\n",
    "\n",
    "    S_Ind = np.argsort(XY)\n",
    "    XY_Sorted = XY[S_Ind]\n",
    "    X2_Sorted = X2[S_Ind]\n",
    "    Y2_Sorted = Y2[S_Ind]\n",
    "\n",
    "    uo = 0\n",
    "    down = 0\n",
    "    Res = 0\n",
    "    E_CDF = 0\n",
    "    F_CDF = 0\n",
    "    power = 1\n",
    "\n",
    "    for ii in range(0, n-2):\n",
    "        E_CDF = E_CDF + X2_Sorted[ii]\n",
    "        F_CDF = F_CDF + Y2_Sorted[ii]\n",
    "        if XY_Sorted[ii+1] != XY_Sorted[ii]: height = F_CDF-E_CDF\n",
    "        if height > up: up = height\n",
    "        if height < up: down = height\n",
    "\n",
    "    K_Dist = abs(down)**power + abs(up)**power\n",
    "    \n",
    "    return K_Dist\n",
    "\n",
    "def Kuiper_Dist_PVal(XX, YY):\n",
    "    import random\n",
    "    nboots = 100\n",
    "    KD = Kuiper_Dist(XX,YY)\n",
    "    na = len(XX)\n",
    "    nb = len(YY)\n",
    "    n = na + nb\n",
    "    comb = np.concatenate([XX,YY])\n",
    "    reps = 0\n",
    "    bigger = 0\n",
    "    for ii in range(1, nboots):\n",
    "        e = random.sample(range(n), na)\n",
    "        f = random.sample(range(n), nb)\n",
    "        boost_KD = Kuiper_Dist(comb[e],comb[f]);\n",
    "        if (boost_KD > KD):\n",
    "            bigger = 1 + bigger\n",
    "            \n",
    "    pVal = bigger/nboots;\n",
    "\n",
    "    return pVal, KD\n",
    "\n",
    "C_num = 3\n",
    "\n",
    "# Separating Wrong Responses of the CNN Classifier\n",
    "X_test_wrong, y_test_wrong = X_test[np.where(y_test != y_pred)], y_test[np.where(y_test != y_pred)]\n",
    "\n",
    "# print(X_test_wrong.shape)\n",
    "\n",
    "# Finding Wrong Decisions for Label 2 (just an example)\n",
    "X_test_wrong3, y_test_wrong3 = X_test_wrong[np.where(y_test_wrong == C_num)], y_test_wrong[np.where(y_test_wrong == C_num)]\n",
    "\n",
    "# print(X_test_wrong1.shape)\n",
    "\n",
    "X_train3 = X_train[np.where(y_train[:,C_num] == 1)]\n",
    "\n",
    "fig4, ax44 = pyplot.subplots(1,3, figsize = (20,6))\n",
    "#fig5, ax55 = pyplot.subplots(1,3, figsize = (20,6))\n",
    "\n",
    "for ii, c_ax44 in enumerate(ax44.flatten()):\n",
    "    # Comparing X_train for Label == 2 with X_Test_wronge for Label == 2\n",
    "    xxx, yyy= X_train3[:,:,:,ii], X_test_wrong3[:,:,:,ii]\n",
    "    xxx_2   = np.array([yy.flatten() for yy in xxx])\n",
    "    yyy_2   = np.array([yy.flatten() for yy in yyy]) \n",
    "    \n",
    "    KD = np.zeros(900)\n",
    "    pVal = np.zeros(900)\n",
    "    \n",
    "      \n",
    "    for kk in range(1, 900):\n",
    "        KD[kk], pVal[kk] = CVM_Dist_PVal(xxx_2[:30,kk], yyy_2[:30,kk]) \n",
    "        \n",
    "    KD2 = KD  \n",
    "        \n",
    "    print(1 - KD2[KD2 != 0].mean())\n",
    "    \n",
    "    KD[pVal > 0.05] = 0\n",
    "    \n",
    "    J,Q     = xxx_2.shape\n",
    "    z       = KD2\n",
    "    zstar   = pVal #WD.mean()\n",
    "    z0      = np.zeros(Q)\n",
    "    z0      = z\n",
    "    z1      = z0.copy()\n",
    "    #z1[np.abs(z1)<zstar] = 0\n",
    "    Z0      = np.reshape(z1, (30,30))\n",
    "    #Z0i     = Z0.copy()\n",
    "    #Z0i[np.abs(Z0i)<zstar] = 0\n",
    "    #ZZ      = np.hstack( [Z0, Z0i] )\n",
    "    \n",
    "    z2      = KD\n",
    "    Z02     = np.reshape(z2, (30,30))\n",
    "    \n",
    "    c_ax44.imshow(Z0, 'jet') # Can be replaced with Z0i\n",
    "    c_ax44.set_title('RGB: {} of Set {} vs. Set {}'.format(ii, 1,2))\n",
    "    c_ax44.axis('off')\n",
    "    \n",
    "fig5, ax55 = pyplot.subplots(1,3, figsize = (20,6))     \n",
    "\n",
    "for ii, c_ax55 in enumerate(ax55.flatten()):\n",
    "    c_ax55.imshow(X_train3[1,:,:,ii], interpolation = 'none')\n",
    "    c_ax55.set_title('RGB: {} -- Test {}'.format(ii+1, 3))\n",
    "    c_ax55.axis(\"off\")\n",
    "    \n",
    "fig4, ax8 = pyplot.subplots(1,3, figsize = (20,6))     \n",
    "\n",
    "for ii, c_ax in enumerate(ax8.flatten()):\n",
    "    c_ax.imshow(X_test_wrong3[1,:,:,ii], interpolation = 'none')\n",
    "    c_ax.set_title('RGB: {} -- Test {}'.format(ii+1, y_test_wrong3[1]))\n",
    "    c_ax.axis('off')  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculating Wilson Interval Confidence\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "435\n",
      "15\n",
      "0.9655172413793104\n",
      "(0.9260910660147941, 0.9853196002242328)\n"
     ]
    }
   ],
   "source": [
    "# Kläs, M., & Sembach, L. (2019). Uncertainty wrappers for data-driven models. In International Conference on Computer Safety, Reliability, and Security. Springer.\n",
    "from math import sqrt\n",
    "def wilson(p, n, z = 3.29): # The z-score for a 95% confidence interval is 1.96.\n",
    "    denominator = 1 + z**2/n\n",
    "    centre_adjusted_probability = p + z*z / (2*n)\n",
    "    adjusted_standard_deviation = sqrt((p*(1 - p) + z*z / (4*n)) / n)\n",
    "    \n",
    "    lower_bound = (centre_adjusted_probability - z*adjusted_standard_deviation) / denominator\n",
    "    upper_bound = (centre_adjusted_probability + z*adjusted_standard_deviation) / denominator\n",
    "    \n",
    "    return (lower_bound, upper_bound)\n",
    "\n",
    "\n",
    "X_test_correct, y_test_correct = X_test[np.where(y_test == y_pred)], y_test[np.where(y_test == y_pred)]\n",
    "\n",
    "X_test_correct3, y_test_correct3 = X_test_correct[np.where(y_test_correct == 3)], y_test_correct[np.where(y_test_correct == 3)]\n",
    "\n",
    "print(X_test_correct3.shape[0])\n",
    "print(X_test_wrong3.shape[0])\n",
    "\n",
    "print(1 - X_test_wrong3.shape[0]/X_test_correct3.shape[0])\n",
    "\n",
    "Negative = X_test_wrong3.shape[0] \n",
    "total = X_test_wrong3.shape[0] + X_test_correct3.shape[0] \n",
    "p  = 1 - Negative / total\n",
    "\n",
    "print(wilson(p, total))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
